Docs¶

All Templates¶

class greykite.framework.templates.forecaster.Forecaster(model_template_enum: Type[enum.Enum] = <enum 'ModelTemplateEnum'>, default_model_template_name: str = 'SILVERKITE')[source]¶

The main entry point to create a forecast.

Call the run_forecast_config method to create a forecast. It takes a dataset and forecast configuration parameters.

Notes

This class can create forecasts using any of the model templates in ModelTemplateEnum. Model templates provide suitable default values for the available forecast estimators depending on the data characteristics.

The model template is selected via the config.model_template parameter to run_forecast_config.

To add your own custom algorithms or template classes in our framework, pass model_template_enum and default_model_template_name to the constructor.

model_template_enum: Type[Enum]¶: The available template names. An Enum class where names are template names, and values are of type ModelTemplate.

default_model_template_name: str¶: The default template name if not provided by config.model_template. Should be a name in model_template_enum. Used by __get_template_class.

template_class: Optional[Type[TemplateInterface]]¶: Template class used. Must implement TemplateInterface and be one of the classes in self.model_template_enum. Available for debugging purposes. Set by run_forecast_config.

template: Optional[TemplateInterface]¶

Instance of template_class used to run the forecast. See the docstring of the specific template class used.

SimpleSilverkiteTemplate

SilverkiteTemplate

ProphetTemplate

etc.

Available for debugging purposes. Set by run_forecast_config.

config: Optional[ForecastConfig]¶: ForecastConfig passed to the template class. Set by run_forecast_config.

pipeline_params: Optional[Dict]¶: Parameters used to call forecast_pipeline. Available for debugging purposes. Set by run_forecast_config.

forecast_result: Optional[ForecastResult]¶: The forecast result, returned by forecast_pipeline. Set by run_forecast_config.

apply_forecast_config(df: pandas.core.frame.DataFrame, config: Optional[greykite.framework.templates.autogen.forecast_config.ForecastConfig] = None) → Dict[source]¶

Fetches pipeline parameters from the df and config, but does not run the pipeline to generate a forecast.

run_forecast_config calls this function and also runs the forecast pipeline.

Available for debugging purposes to check pipeline parameters before running a forecast. Sets these attributes for debugging:

pipeline_params : the parameters passed to forecast_pipeline.

template_class, template : the template class used to generate the pipeline parameters.

config : the ForecastConfig passed as input to template class, to translate into pipeline parameters.

Provides basic validation on the compatibility of config.model_template with config.model_components_param.

Parameters

df (pandas.DataFrame) – Timeseries data to forecast. Contains columns [time_col, value_col], and optional regressor columns Regressor columns should include future values for prediction
config (ForecastConfig or None) – Config object for template class to use. See ForecastConfig.

Returns

pipeline_params – Input to forecast_pipeline.

Return type

dict [str, any]

run_forecast_config(df: pandas.core.frame.DataFrame, config: Optional[greykite.framework.templates.autogen.forecast_config.ForecastConfig] = None) → greykite.framework.pipeline.pipeline.ForecastResult [source]¶

Creates a forecast from input data and config. The result is also stored as self.forecast_result.

Parameters

df (pandas.DataFrame) – Timeseries data to forecast. Contains columns [time_col, value_col], and optional regressor columns Regressor columns should include future values for prediction
config (ForecastConfig) – Config object for template class to use. See ForecastConfig.

Returns

forecast_result – Forecast result, an object of type ForecastResult.

The output of forecast_pipeline, according to the df and config configuration parameters.

Return type

ForecastResult

run_forecast_json(df: pandas.core.frame.DataFrame, json_str: str = '{}') → greykite.framework.pipeline.pipeline.ForecastResult [source]¶

Calls forecast_pipeline according to the json_str configuration parameters.

Parameters

df (pandas.DataFrame) – Timeseries data to forecast. Contains columns [time_col, value_col], and optional regressor columns Regressor columns should include future values for prediction
json_str (str) – Json string of the config object for Forecast to use. See ForecastConfig.

Returns

forecast_result – Forecast result. The output of forecast_pipeline, called using the template class with specified configuration. See ForecastResult for details.

Return type

ForecastResult

dump_forecast_result(destination_dir, object_name='object', dump_design_info=True, overwrite_exist_dir=False)[source]¶

Dumps self.forecast_result to local pickle files.

Parameters

destination_dir (str) – The pickle destination directory.
object_name (str) – The stored file name.
dump_design_info (bool, default True) – Whether to dump design info. Design info is a patsy class that includes the design matrix information. It takes longer to dump design info.
overwrite_exist_dir (bool, default False) – What to do when destination_dir already exists. Removes the original directory when exists, if set to True.

Returns

Return type

This function writes to local files and does not return anything.

load_forecast_result(source_dir, load_design_info=True)[source]¶

Loads self.forecast_result from local files created by self.dump_result.

Parameters

source_dir (str) – The source file directory.
load_design_info (bool, default True) – Whether to load design info. Design info is a patsy class that includes the design matrix information. It takes longer to load design info.

class greykite.framework.templates.model_templates.ModelTemplate(template_class: Type[greykite.framework.templates.template_interface.TemplateInterface], description: str)[source]¶

A model template consists of a template class, a description, and a name.

This class holds the template class and description. The model template name is the member name in greykite.framework.templates.model_templates.ModelTemplateEnum.

template_class: Type[greykite.framework.templates.template_interface.TemplateInterface]¶: A class that implements the template interface.

description: str¶: A description of the model template.

class greykite.framework.templates.model_templates.ModelTemplateEnum(value)[source]¶

Available model templates.

Enumerates the possible values for the model_template attribute of ForecastConfig.

The value has type ModelTemplate which contains:

the template class that recognizes the model_template. Template classes implement the TemplateInterface interface.

a plain-text description of what the model_template is for,

The description should be unique across enum members. The template class can be shared, because a template class can recognize multiple model templates. For example, the same template class may use different default values for ForecastConfig.model_components_param depending on ForecastConfig.model_template.

Notes

The template classes SilverkiteTemplate and ProphetTemplate recognize only the model templates explicitly enumerated here.

However, the SimpleSilverkiteTemplate template class allows additional model templates to be specified generically. Any object of type SimpleSilverkiteTemplateOptions can be used as the model_template. These generic model templates are valid but not enumerated here.

SILVERKITE = ModelTemplate(template_class=<class 'greykite.framework.templates.simple_silverkite_template.SimpleSilverkiteTemplate'>, description='Silverkite model with automatic growth, seasonality, holidays, and interactions. Best for hourly and daily frequencies.Uses `SimpleSilverkiteEstimator`.')¶: Silverkite model with automatic growth, seasonality, holidays, and interactions. Best for hourly and daily frequencies. Uses SimpleSilverkiteEstimator.

SILVERKITE_WITH_AR = ModelTemplate(template_class=<class 'greykite.framework.templates.simple_silverkite_template.SimpleSilverkiteTemplate'>, description='Has the same config as ``SILVERKITE`` except for adding autoregression. Best for short-term daily forecasts. Uses `SimpleSilverkiteEstimator`.')¶: Has the same config as SILVERKITE except for adding autoregression. Best for short-term daily forecasts. Uses SimpleSilverkiteEstimator.

SILVERKITE_DAILY_1_CONFIG_1 = ModelTemplate(template_class=<class 'greykite.framework.templates.simple_silverkite_template.SimpleSilverkiteTemplate'>, description='Config 1 in template ``SILVERKITE_DAILY_1``. Compared to ``SILVERKITE``, it adds change points and uses parameters specifically tuned for daily data and 1-day forecast.')¶: Config 1 in template SILVERKITE_DAILY_1. Compared to SILVERKITE, it adds change points and uses parameters specifically tuned for daily data and 1-day forecast.

SILVERKITE_DAILY_1_CONFIG_2 = ModelTemplate(template_class=<class 'greykite.framework.templates.simple_silverkite_template.SimpleSilverkiteTemplate'>, description='Config 2 in template ``SILVERKITE_DAILY_1``. Compared to ``SILVERKITE``, it adds change points and uses parameters specifically tuned for daily data and 1-day forecast.')¶: Config 2 in template SILVERKITE_DAILY_1. Compared to SILVERKITE, it adds change points and uses parameters specifically tuned for daily data and 1-day forecast.

SILVERKITE_DAILY_1_CONFIG_3 = ModelTemplate(template_class=<class 'greykite.framework.templates.simple_silverkite_template.SimpleSilverkiteTemplate'>, description='Config 3 in template ``SILVERKITE_DAILY_1``. Compared to ``SILVERKITE``, it adds change points and uses parameters specifically tuned for daily data and 1-day forecast.')¶: Config 3 in template SILVERKITE_DAILY_1. Compared to SILVERKITE, it adds change points and uses parameters specifically tuned for daily data and 1-day forecast.

SILVERKITE_DAILY_1 = ModelTemplate(template_class=<class 'greykite.framework.templates.simple_silverkite_template.SimpleSilverkiteTemplate'>, description='Silverkite model specifically tuned for daily data and 1-day forecast. Contains 3 candidate configs for grid search, optimized the seasonality and changepoint parameters.')¶: Silverkite model specifically tuned for daily data and 1-day forecast. Contains 3 candidate configs for grid search, optimized the seasonality and changepoint parameters.

SILVERKITE_DAILY_90 = ModelTemplate(template_class=<class 'greykite.framework.templates.simple_silverkite_template.SimpleSilverkiteTemplate'>, description='Silverkite model specifically tuned for daily data with 90 days forecast horizon. Contains 4 hyperparameter combinations for grid search. Uses `SimpleSilverkiteEstimator`.')¶: Silverkite model specifically tuned for daily data with 90 days forecast horizon. Contains 4 hyperparameter combinations for grid search. Uses SimpleSilverkiteEstimator.

SILVERKITE_WEEKLY = ModelTemplate(template_class=<class 'greykite.framework.templates.simple_silverkite_template.SimpleSilverkiteTemplate'>, description='Silverkite model specifically tuned for weekly data. Contains 4 hyperparameter combinations for grid search. Uses `SimpleSilverkiteEstimator`.')¶: Silverkite model specifically tuned for weekly data. Contains 4 hyperparameter combinations for grid search. Uses SimpleSilverkiteEstimator.

SILVERKITE_HOURLY_1 = ModelTemplate(template_class=<class 'greykite.framework.templates.simple_silverkite_template.SimpleSilverkiteTemplate'>, description='Silverkite model specifically tuned for hourly data with 1 hour forecast horizon. Contains 4 hyperparameter combinations for grid search. Uses `SimpleSilverkiteEstimator`.')¶: Silverkite model specifically tuned for hourly data with 1 hour forecast horizon. Contains 4 hyperparameter combinations for grid search. Uses SimpleSilverkiteEstimator.

SILVERKITE_HOURLY_24 = ModelTemplate(template_class=<class 'greykite.framework.templates.simple_silverkite_template.SimpleSilverkiteTemplate'>, description='Silverkite model specifically tuned for hourly data with 24 hours (1 day) forecast horizon. Contains 4 hyperparameter combinations for grid search. Uses `SimpleSilverkiteEstimator`.')¶: Silverkite model specifically tuned for hourly data with 24 hours (1 day) forecast horizon. Contains 4 hyperparameter combinations for grid search. Uses SimpleSilverkiteEstimator.

SILVERKITE_HOURLY_168 = ModelTemplate(template_class=<class 'greykite.framework.templates.simple_silverkite_template.SimpleSilverkiteTemplate'>, description='Silverkite model specifically tuned for hourly data with 168 hours (1 week) forecast horizon. Contains 4 hyperparameter combinations for grid search. Uses `SimpleSilverkiteEstimator`.')¶: Silverkite model specifically tuned for hourly data with 168 hours (1 week) forecast horizon. Contains 4 hyperparameter combinations for grid search. Uses SimpleSilverkiteEstimator.

SILVERKITE_HOURLY_336 = ModelTemplate(template_class=<class 'greykite.framework.templates.simple_silverkite_template.SimpleSilverkiteTemplate'>, description='Silverkite model specifically tuned for hourly data with 336 hours (2 weeks) forecast horizon. Contains 4 hyperparameter combinations for grid search. Uses `SimpleSilverkiteEstimator`.')¶: Silverkite model specifically tuned for hourly data with 336 hours (2 weeks) forecast horizon. Contains 4 hyperparameter combinations for grid search. Uses SimpleSilverkiteEstimator.

SILVERKITE_EMPTY = ModelTemplate(template_class=<class 'greykite.framework.templates.simple_silverkite_template.SimpleSilverkiteTemplate'>, description='Silverkite model with no component included by default. Fits only a constant intercept. Select and customize this template to add only the terms you want. Uses `SimpleSilverkiteEstimator`.')¶: Silverkite model with no component included by default. Fits only a constant intercept. Select and customize this template to add only the terms you want. Uses SimpleSilverkiteEstimator.

SK = ModelTemplate(template_class=<class 'greykite.framework.templates.silverkite_template.SilverkiteTemplate'>, description='Silverkite model with low-level interface. For flexible model tuning if SILVERKITE template is not flexible enough. Not for use out-of-the-box: customization is needed for good performance. Uses `SilverkiteEstimator`.')¶: Silverkite model with low-level interface. For flexible model tuning if SILVERKITE template is not flexible enough. Not for use out-of-the-box: customization is needed for good performance. Uses SilverkiteEstimator.

PROPHET = ModelTemplate(template_class=<class 'greykite.framework.templates.prophet_template.ProphetTemplate'>, description='Prophet model with growth, seasonality, holidays, additional regressors and prediction intervals. Uses `ProphetEstimator`.')¶: Prophet model with growth, seasonality, holidays, additional regressors and prediction intervals. Uses ProphetEstimator.

AUTO_ARIMA = ModelTemplate(template_class=<class 'greykite.framework.templates.auto_arima_template.AutoArimaTemplate'>, description='Auto ARIMA model with fit and prediction intervals. Uses `AutoArimaEstimator`.')¶: ARIMA model with automatic order selection. Uses AutoArimaEstimator.

SILVERKITE_TWO_STAGE = ModelTemplate(template_class=<class 'greykite.framework.templates.silverkite_multistage_template.SilverkiteMultistageTemplate'>, description="SilverkiteMultistageTemplate's default model template. A two-stage model. The first step takes a longer history and learns the long-term effects, while the second step takes a shorter history and learns the short-term residuals.")¶: SilverkiteMultistage model’s default model template. A two-stage model. ” “The first step takes a longer history and learns the long-term effects, ” “while the second step takes a shorter history and learns the short-term residuals.

SILVERKITE_MULTISTAGE_EMPTY = ModelTemplate(template_class=<class 'greykite.framework.templates.silverkite_multistage_template.SilverkiteMultistageTemplate'>, description='Empty configuration for Silverkite Multistage. All parameters will be exactly what user inputs. Not to be used without overriding.')¶: Empty configuration for Silverkite Multistage. All parameters will be exactly what user inputs. Not to be used without overriding.

class greykite.framework.templates.autogen.forecast_config.ForecastConfig(computation_param: Optional[greykite.framework.templates.autogen.forecast_config.ComputationParam] = None, coverage: Optional[float] = None, evaluation_metric_param: Optional[greykite.framework.templates.autogen.forecast_config.EvaluationMetricParam] = None, evaluation_period_param: Optional[greykite.framework.templates.autogen.forecast_config.EvaluationPeriodParam] = None, forecast_horizon: Optional[int] = None, forecast_one_by_one: Optional[Union[bool, int, List[int]]] = None, metadata_param: Optional[greykite.framework.templates.autogen.forecast_config.MetadataParam] = None, model_components_param: Optional[Union[greykite.framework.templates.autogen.forecast_config.ModelComponentsParam, List[Optional[greykite.framework.templates.autogen.forecast_config.ModelComponentsParam]]]] = None, model_template: Optional[Union[str, dataclasses.dataclass, List[Union[str, dataclasses.dataclass]]]] = None)[source]¶

Config for providing parameters to the Forecast library

computation_param: Optional[greykite.framework.templates.autogen.forecast_config.ComputationParam] = None¶: How to compute the result. See ComputationParam.

coverage: Optional[float] = None¶: Intended coverage of the prediction bands (0.0 to 1.0). If None, the upper/lower predictions are not returned.

evaluation_metric_param: Optional[greykite.framework.templates.autogen.forecast_config.EvaluationMetricParam] = None¶: What metrics to evaluate. See EvaluationMetricParam.

evaluation_period_param: Optional[greykite.framework.templates.autogen.forecast_config.EvaluationPeriodParam] = None¶: How to split data for evaluation. See EvaluationPeriodParam.

forecast_horizon: Optional[int] = None¶: Number of periods to forecast into the future. Must be > 0. If None, default is determined from input data frequency.

forecast_one_by_one: Optional[Union[bool, int, List[int]]] = None¶: The options to activate the forecast one-by-one algorithm. See OneByOneEstimator. Can be boolean, int, of list of int. If int, it has to be less than or equal to the forecast horizon. If list of int, the sum has to be the forecast horizon.

metadata_param: Optional[greykite.framework.templates.autogen.forecast_config.MetadataParam] = None¶: Information about the input data. See MetadataParam.

model_components_param: Optional[Union[greykite.framework.templates.autogen.forecast_config.ModelComponentsParam, List[Optional[greykite.framework.templates.autogen.forecast_config.ModelComponentsParam]]]] = None¶: Parameters to tune the model. Typically a single ModelComponentsParam, but the SimpleSilverkiteTemplate template also allows a list of ModelComponentsParam for grid search. A single ModelComponentsParam corresponds to one grid, and a list corresponds to a list of grids. See ModelComponentsParam.

model_template: Optional[Union[str, dataclasses.dataclass, List[Union[str, dataclasses.dataclass]]]] = None¶: Name of the model template. Typically a single string, but the SimpleSilverkiteTemplate template also allows a list of string for grid search. See ModelTemplateEnum for valid names.

class greykite.framework.templates.autogen.forecast_config.MetadataParam(anomaly_info: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None, date_format: Optional[str] = None, freq: Optional[str] = None, time_col: Optional[str] = None, train_end_date: Optional[str] = None, value_col: Optional[str] = None)[source]¶

Properties of the input data

anomaly_info: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None¶: Anomaly adjustment info. Anomalies in df are corrected before any forecasting is done. If None, no adjustments are made. See forecast_pipeline.

date_format: Optional[str] = None¶: See forecast_pipeline

freq: Optional[str] = None¶: See forecast_pipeline

time_col: Optional[str] = None¶: See forecast_pipeline

train_end_date: Optional[str] = None¶: Last date to use for fitting the model. Forecasts are generated after this date. If None, it is set to the last date with a non-null value in value_col df. See forecast_pipeline.

value_col: Optional[str] = None¶: See forecast_pipeline

class greykite.framework.templates.autogen.forecast_config.EvaluationMetricParam(agg_func: Optional[Callable] = None, agg_periods: Optional[int] = None, cv_report_metrics: Optional[Union[str, List[str]]] = None, cv_selection_metric: Optional[str] = None, null_model_params: Optional[Dict[str, Any]] = None, relative_error_tolerance: Optional[float] = None)[source]¶

What metrics to evaluate

agg_func: Optional[Callable] = None¶: See forecast_pipeline.

agg_periods: Optional[int] = None¶: See forecast_pipeline.

cv_report_metrics: Optional[Union[str, List[str]]] = None¶: See score_func in forecast_pipeline.

cv_selection_metric: Optional[str] = None¶: See score_func in forecast_pipeline.

null_model_params: Optional[Dict[str, Any]] = None¶: See forecast_pipeline.

relative_error_tolerance: Optional[float] = None¶: See forecast_pipeline.

class greykite.framework.templates.autogen.forecast_config.EvaluationPeriodParam(cv_expanding_window: Optional[bool] = None, cv_horizon: Optional[int] = None, cv_max_splits: Optional[int] = None, cv_min_train_periods: Optional[int] = None, cv_periods_between_splits: Optional[int] = None, cv_periods_between_train_test: Optional[int] = None, cv_use_most_recent_splits: Optional[bool] = None, periods_between_train_test: Optional[int] = None, test_horizon: Optional[int] = None)[source]¶

How to split data for evaluation.

cv_expanding_window: Optional[bool] = None¶: See forecast_pipeline.

cv_horizon: Optional[int] = None¶: See forecast_pipeline.

cv_max_splits: Optional[int] = None¶: See forecast_pipeline.

cv_min_train_periods: Optional[int] = None¶: See forecast_pipeline.

cv_periods_between_splits: Optional[int] = None¶: See forecast_pipeline.

cv_periods_between_train_test: Optional[int] = None¶: See forecast_pipeline.

cv_use_most_recent_splits: Optional[bool] = None¶: See forecast_pipeline.

periods_between_train_test: Optional[int] = None¶: See forecast_pipeline

test_horizon: Optional[int] = None¶: See forecast_pipeline.

class greykite.framework.templates.autogen.forecast_config.ModelComponentsParam(autoregression: Optional[Dict[str, Any]] = None, changepoints: Optional[Dict[str, Any]] = None, custom: Optional[Dict[str, Any]] = None, events: Optional[Dict[str, Any]] = None, growth: Optional[Dict[str, Any]] = None, hyperparameter_override: Optional[Union[Dict, List[Optional[Dict]]]] = None, regressors: Optional[Dict[str, Any]] = None, lagged_regressors: Optional[Dict[str, Any]] = None, seasonality: Optional[Dict[str, Any]] = None, uncertainty: Optional[Dict[str, Any]] = None)[source]¶

Parameters to tune the model.

autoregression: Optional[Dict[str, Any]] = None¶: For modeling autoregression, see template for details

changepoints: Optional[Dict[str, Any]] = None¶: For modeling changepoints, see template for details

custom: Optional[Dict[str, Any]] = None¶: Additional parameters used by template, see template for details

events: Optional[Dict[str, Any]] = None¶: For modeling events, see template for details

growth: Optional[Dict[str, Any]] = None¶: For modeling growth (trend), see template for details

hyperparameter_override: Optional[Union[Dict, List[Optional[Dict]]]] = None¶: After the above model components are used to create a hyperparameter grid, the result is updated by this dictionary, to create new keys or override existing ones. Allows for complete customization of the grid search.

regressors: Optional[Dict[str, Any]] = None¶: For modeling regressors, see template for details

lagged_regressors: Optional[Dict[str, Any]] = None¶: For modeling lagged regressors, see template for details

seasonality: Optional[Dict[str, Any]] = None¶: For modeling seasonality, see template for details

uncertainty: Optional[Dict[str, Any]] = None¶: For modeling uncertainty, see template for details

class greykite.framework.templates.autogen.forecast_config.ComputationParam(hyperparameter_budget: Optional[int] = None, n_jobs: Optional[int] = None, verbose: Optional[int] = None)[source]¶

How to compute the result.

hyperparameter_budget: Optional[int] = None¶: See forecast_pipeline.

n_jobs: Optional[int] = None¶: See forecast_pipeline.

verbose: Optional[int] = None¶: See forecast_pipeline.

Silverkite Template¶

class greykite.framework.templates.simple_silverkite_template.SimpleSilverkiteTemplate(constants: greykite.framework.templates.simple_silverkite_template_config.SimpleSilverkiteTemplateConstants = SimpleSilverkiteTemplateConstants(COMMON_MODELCOMPONENTPARAM_PARAMETERS={'SEAS': {'HOURLY': {'LT': {'yearly_seasonality': 8, 'quarterly_seasonality': 0, 'monthly_seasonality': 0, 'weekly_seasonality': 3, 'daily_seasonality': 5}, 'NM': {'yearly_seasonality': 15, 'quarterly_seasonality': 0, 'monthly_seasonality': 0, 'weekly_seasonality': 4, 'daily_seasonality': 8}, 'HV': {'yearly_seasonality': 25, 'quarterly_seasonality': 0, 'monthly_seasonality': 0, 'weekly_seasonality': 6, 'daily_seasonality': 12}, 'LTQM': {'yearly_seasonality': 8, 'quarterly_seasonality': 2, 'monthly_seasonality': 2, 'weekly_seasonality': 3, 'daily_seasonality': 5}, 'NMQM': {'yearly_seasonality': 15, 'quarterly_seasonality': 3, 'monthly_seasonality': 3, 'weekly_seasonality': 4, 'daily_seasonality': 8}, 'HVQM': {'yearly_seasonality': 25, 'quarterly_seasonality': 4, 'monthly_seasonality': 4, 'weekly_seasonality': 6, 'daily_seasonality': 12}, 'NONE': {'yearly_seasonality': 0, 'quarterly_seasonality': 0, 'monthly_seasonality': 0, 'weekly_seasonality': 0, 'daily_seasonality': 0}}, 'DAILY': {'LT': {'yearly_seasonality': 8, 'quarterly_seasonality': 0, 'monthly_seasonality': 0, 'weekly_seasonality': 3, 'daily_seasonality': 0}, 'NM': {'yearly_seasonality': 15, 'quarterly_seasonality': 0, 'monthly_seasonality': 0, 'weekly_seasonality': 3, 'daily_seasonality': 0}, 'HV': {'yearly_seasonality': 25, 'quarterly_seasonality': 0, 'monthly_seasonality': 0, 'weekly_seasonality': 4, 'daily_seasonality': 0}, 'LTQM': {'yearly_seasonality': 8, 'quarterly_seasonality': 3, 'monthly_seasonality': 2, 'weekly_seasonality': 3, 'daily_seasonality': 0}, 'NMQM': {'yearly_seasonality': 15, 'quarterly_seasonality': 4, 'monthly_seasonality': 4, 'weekly_seasonality': 3, 'daily_seasonality': 0}, 'HVQM': {'yearly_seasonality': 25, 'quarterly_seasonality': 6, 'monthly_seasonality': 4, 'weekly_seasonality': 4, 'daily_seasonality': 0}, 'NONE': {'yearly_seasonality': 0, 'quarterly_seasonality': 0, 'monthly_seasonality': 0, 'weekly_seasonality': 0, 'daily_seasonality': 0}}, 'WEEKLY': {'LT': {'yearly_seasonality': 8, 'quarterly_seasonality': 0, 'monthly_seasonality': 0, 'weekly_seasonality': 0, 'daily_seasonality': 0}, 'NM': {'yearly_seasonality': 15, 'quarterly_seasonality': 0, 'monthly_seasonality': 0, 'weekly_seasonality': 0, 'daily_seasonality': 0}, 'HV': {'yearly_seasonality': 25, 'quarterly_seasonality': 0, 'monthly_seasonality': 0, 'weekly_seasonality': 0, 'daily_seasonality': 0}, 'LTQM': {'yearly_seasonality': 8, 'quarterly_seasonality': 2, 'monthly_seasonality': 2, 'weekly_seasonality': 0, 'daily_seasonality': 0}, 'NMQM': {'yearly_seasonality': 15, 'quarterly_seasonality': 3, 'monthly_seasonality': 3, 'weekly_seasonality': 0, 'daily_seasonality': 0}, 'HVQM': {'yearly_seasonality': 25, 'quarterly_seasonality': 4, 'monthly_seasonality': 4, 'weekly_seasonality': 0, 'daily_seasonality': 0}, 'NONE': {'yearly_seasonality': 0, 'quarterly_seasonality': 0, 'monthly_seasonality': 0, 'weekly_seasonality': 0, 'daily_seasonality': 0}}}, 'GR': {'LINEAR': {'growth_term': 'linear'}, 'NONE': {'growth_term': None}}, 'CP': {'HOURLY': {'LT': {'method': 'auto', 'resample_freq': 'D', 'regularization_strength': 0.6, 'potential_changepoint_distance': '7D', 'no_changepoint_distance_from_end': '30D', 'yearly_seasonality_order': 15, 'yearly_seasonality_change_freq': None}, 'NM': {'method': 'auto', 'resample_freq': 'D', 'regularization_strength': 0.5, 'potential_changepoint_distance': '15D', 'no_changepoint_distance_from_end': '30D', 'yearly_seasonality_order': 15, 'yearly_seasonality_change_freq': '365D'}, 'HV': {'method': 'auto', 'resample_freq': 'D', 'regularization_strength': 0.3, 'potential_changepoint_distance': '15D', 'no_changepoint_distance_from_end': '30D', 'yearly_seasonality_order': 15, 'yearly_seasonality_change_freq': '365D'}, 'NONE': None}, 'DAILY': {'LT': {'method': 'auto', 'resample_freq': '7D', 'regularization_strength': 0.6, 'potential_changepoint_distance': '15D', 'no_changepoint_distance_from_end': '90D', 'yearly_seasonality_order': 15, 'yearly_seasonality_change_freq': None}, 'NM': {'method': 'auto', 'resample_freq': '7D', 'regularization_strength': 0.5, 'potential_changepoint_distance': '15D', 'no_changepoint_distance_from_end': '180D', 'yearly_seasonality_order': 15, 'yearly_seasonality_change_freq': '365D'}, 'HV': {'method': 'auto', 'resample_freq': '7D', 'regularization_strength': 0.3, 'potential_changepoint_distance': '15D', 'no_changepoint_distance_from_end': '180D', 'yearly_seasonality_order': 15, 'yearly_seasonality_change_freq': '365D'}, 'NONE': None}, 'WEEKLY': {'LT': {'method': 'auto', 'resample_freq': '7D', 'regularization_strength': 0.6, 'potential_changepoint_distance': '14D', 'no_changepoint_distance_from_end': '180D', 'yearly_seasonality_order': 15, 'yearly_seasonality_change_freq': None}, 'NM': {'method': 'auto', 'resample_freq': '7D', 'regularization_strength': 0.5, 'potential_changepoint_distance': '14D', 'no_changepoint_distance_from_end': '180D', 'yearly_seasonality_order': 15, 'yearly_seasonality_change_freq': '365D'}, 'HV': {'method': 'auto', 'resample_freq': '7D', 'regularization_strength': 0.3, 'potential_changepoint_distance': '14D', 'no_changepoint_distance_from_end': '180D', 'yearly_seasonality_order': 15, 'yearly_seasonality_change_freq': '365D'}, 'NONE': None}}, 'HOL': {'SP1': {'holidays_to_model_separately': 'auto', 'holiday_lookup_countries': 'auto', 'holiday_pre_num_days': 1, 'holiday_post_num_days': 1, 'holiday_pre_post_num_dict': None, 'daily_event_df_dict': None}, 'SP2': {'holidays_to_model_separately': 'auto', 'holiday_lookup_countries': 'auto', 'holiday_pre_num_days': 2, 'holiday_post_num_days': 2, 'holiday_pre_post_num_dict': None, 'daily_event_df_dict': None}, 'SP4': {'holidays_to_model_separately': 'auto', 'holiday_lookup_countries': 'auto', 'holiday_pre_num_days': 4, 'holiday_post_num_days': 4, 'holiday_pre_post_num_dict': None, 'daily_event_df_dict': None}, 'TG': {'holidays_to_model_separately': [], 'holiday_lookup_countries': 'auto', 'holiday_pre_num_days': 3, 'holiday_post_num_days': 3, 'holiday_pre_post_num_dict': None, 'daily_event_df_dict': None}, 'NONE': {'holidays_to_model_separately': [], 'holiday_lookup_countries': [], 'holiday_pre_num_days': 0, 'holiday_post_num_days': 0, 'holiday_pre_post_num_dict': None, 'daily_event_df_dict': None}}, 'FEASET': {'AUTO': 'auto', 'ON': True, 'OFF': False}, 'ALGO': {'LINEAR': {'fit_algorithm': 'linear', 'fit_algorithm_params': None}, 'RIDGE': {'fit_algorithm': 'ridge', 'fit_algorithm_params': None}, 'SGD': {'fit_algorithm': 'sgd', 'fit_algorithm_params': None}, 'LASSO': {'fit_algorithm': 'lasso', 'fit_algorithm_params': None}}, 'AR': {'AUTO': {'autoreg_dict': 'auto', 'simulation_num': 10}, 'OFF': {'autoreg_dict': None, 'simulation_num': 10}}, 'DSI': {'HOURLY': {'AUTO': 5, 'OFF': 0}, 'DAILY': {'AUTO': 0, 'OFF': 0}, 'WEEKLY': {'AUTO': 0, 'OFF': 0}}, 'WSI': {'HOURLY': {'AUTO': 2, 'OFF': 0}, 'DAILY': {'AUTO': 2, 'OFF': 0}, 'WEEKLY': {'AUTO': 0, 'OFF': 0}}}, MULTI_TEMPLATES={'SILVERKITE_DAILY_1': ['SILVERKITE_DAILY_1_CONFIG_1', 'SILVERKITE_DAILY_1_CONFIG_2', 'SILVERKITE_DAILY_1_CONFIG_3'], 'SILVERKITE_DAILY_90': ['DAILY_SEAS_LTQM_GR_LINEAR_CP_LT_HOL_SP2_FEASET_AUTO_ALGO_LINEAR_AR_OFF_DSI_AUTO_WSI_AUTO', 'DAILY_SEAS_LTQM_GR_LINEAR_CP_NONE_HOL_SP2_FEASET_AUTO_ALGO_LINEAR_AR_OFF_DSI_AUTO_WSI_AUTO', 'DAILY_SEAS_LTQM_GR_LINEAR_CP_LT_HOL_SP2_FEASET_AUTO_ALGO_RIDGE_AR_OFF_DSI_AUTO_WSI_AUTO', 'DAILY_SEAS_NM_GR_LINEAR_CP_LT_HOL_SP4_FEASET_AUTO_ALGO_RIDGE_AR_OFF_DSI_AUTO_WSI_AUTO'], 'SILVERKITE_WEEKLY': ['WEEKLY_SEAS_NM_GR_LINEAR_CP_NONE_HOL_NONE_FEASET_OFF_ALGO_LINEAR_AR_OFF_DSI_AUTO_WSI_AUTO', 'WEEKLY_SEAS_NM_GR_LINEAR_CP_LT_HOL_NONE_FEASET_OFF_ALGO_LINEAR_AR_OFF_DSI_AUTO_WSI_AUTO', 'WEEKLY_SEAS_HV_GR_LINEAR_CP_NM_HOL_NONE_FEASET_OFF_ALGO_RIDGE_AR_OFF_DSI_AUTO_WSI_AUTO', 'WEEKLY_SEAS_HV_GR_LINEAR_CP_LT_HOL_NONE_FEASET_OFF_ALGO_RIDGE_AR_OFF_DSI_AUTO_WSI_AUTO'], 'SILVERKITE_HOURLY_1': ['HOURLY_SEAS_LT_GR_LINEAR_CP_NONE_HOL_TG_FEASET_AUTO_ALGO_LINEAR_AR_AUTO', 'HOURLY_SEAS_NM_GR_LINEAR_CP_LT_HOL_SP4_FEASET_AUTO_ALGO_LINEAR_AR_AUTO', 'HOURLY_SEAS_LT_GR_LINEAR_CP_NM_HOL_SP4_FEASET_OFF_ALGO_RIDGE_AR_AUTO', 'HOURLY_SEAS_NM_GR_LINEAR_CP_NM_HOL_SP1_FEASET_AUTO_ALGO_RIDGE_AR_AUTO'], 'SILVERKITE_HOURLY_24': ['HOURLY_SEAS_LT_GR_LINEAR_CP_NM_HOL_SP4_FEASET_AUTO_ALGO_RIDGE_AR_AUTO', 'HOURLY_SEAS_LT_GR_LINEAR_CP_NONE_HOL_SP4_FEASET_AUTO_ALGO_RIDGE_AR_AUTO', 'HOURLY_SEAS_NM_GR_LINEAR_CP_LT_HOL_SP1_FEASET_OFF_ALGO_LINEAR_AR_AUTO', 'HOURLY_SEAS_NM_GR_LINEAR_CP_NM_HOL_SP4_FEASET_AUTO_ALGO_RIDGE_AR_AUTO'], 'SILVERKITE_HOURLY_168': ['HOURLY_SEAS_LT_GR_LINEAR_CP_LT_HOL_SP4_FEASET_AUTO_ALGO_RIDGE_AR_OFF', 'HOURLY_SEAS_LT_GR_LINEAR_CP_LT_HOL_SP2_FEASET_AUTO_ALGO_RIDGE_AR_OFF', 'HOURLY_SEAS_NM_GR_LINEAR_CP_NONE_HOL_SP4_FEASET_OFF_ALGO_LINEAR_AR_AUTO', 'HOURLY_SEAS_NM_GR_LINEAR_CP_NM_HOL_SP1_FEASET_AUTO_ALGO_RIDGE_AR_OFF'], 'SILVERKITE_HOURLY_336': ['HOURLY_SEAS_LT_GR_LINEAR_CP_LT_HOL_SP2_FEASET_AUTO_ALGO_RIDGE_AR_OFF', 'HOURLY_SEAS_LT_GR_LINEAR_CP_LT_HOL_SP4_FEASET_AUTO_ALGO_RIDGE_AR_OFF', 'HOURLY_SEAS_NM_GR_LINEAR_CP_LT_HOL_SP1_FEASET_AUTO_ALGO_LINEAR_AR_OFF', 'HOURLY_SEAS_NM_GR_LINEAR_CP_NM_HOL_SP1_FEASET_AUTO_ALGO_LINEAR_AR_AUTO']}, SILVERKITE=ModelComponentsParam(autoregression={'autoreg_dict': None, 'simulation_num': 10}, changepoints={'changepoints_dict': None, 'seasonality_changepoints_dict': None}, custom={'fit_algorithm_dict': {'fit_algorithm': 'ridge', 'fit_algorithm_params': None}, 'feature_sets_enabled': 'auto', 'max_daily_seas_interaction_order': 5, 'max_weekly_seas_interaction_order': 2, 'extra_pred_cols': [], 'drop_pred_cols': None, 'explicit_pred_cols': None, 'min_admissible_value': None, 'max_admissible_value': None, 'regression_weight_col': None, 'normalize_method': None}, events={'holidays_to_model_separately': 'auto', 'holiday_lookup_countries': 'auto', 'holiday_pre_num_days': 2, 'holiday_post_num_days': 2, 'holiday_pre_post_num_dict': None, 'daily_event_df_dict': None}, growth={'growth_term': 'linear'}, hyperparameter_override=None, regressors={'regressor_cols': []}, lagged_regressors={'lagged_regressor_dict': None}, seasonality={'yearly_seasonality': 'auto', 'quarterly_seasonality': 'auto', 'monthly_seasonality': 'auto', 'weekly_seasonality': 'auto', 'daily_seasonality': 'auto'}, uncertainty={'uncertainty_dict': None}), SILVERKITE_WITH_AR=ModelComponentsParam(autoregression={'autoreg_dict': 'auto', 'simulation_num': 10}, changepoints={'changepoints_dict': None, 'seasonality_changepoints_dict': None}, custom={'fit_algorithm_dict': {'fit_algorithm': 'ridge', 'fit_algorithm_params': None}, 'feature_sets_enabled': 'auto', 'max_daily_seas_interaction_order': 5, 'max_weekly_seas_interaction_order': 2, 'extra_pred_cols': [], 'drop_pred_cols': None, 'explicit_pred_cols': None, 'min_admissible_value': None, 'max_admissible_value': None, 'regression_weight_col': None, 'normalize_method': None}, events={'holidays_to_model_separately': 'auto', 'holiday_lookup_countries': 'auto', 'holiday_pre_num_days': 2, 'holiday_post_num_days': 2, 'holiday_pre_post_num_dict': None, 'daily_event_df_dict': None}, growth={'growth_term': 'linear'}, hyperparameter_override=None, regressors={'regressor_cols': []}, lagged_regressors={'lagged_regressor_dict': None}, seasonality={'yearly_seasonality': 'auto', 'quarterly_seasonality': 'auto', 'monthly_seasonality': 'auto', 'weekly_seasonality': 'auto', 'daily_seasonality': 'auto'}, uncertainty={'uncertainty_dict': None}), SILVERKITE_DAILY_1_CONFIG_1=ModelComponentsParam(autoregression={'autoreg_dict': 'auto', 'simulation_num': 10}, changepoints={'changepoints_dict': {'method': 'auto', 'resample_freq': '7D', 'regularization_strength': 0.809, 'potential_changepoint_distance': '7D', 'no_changepoint_distance_from_end': '7D', 'yearly_seasonality_order': 8, 'yearly_seasonality_change_freq': None}, 'seasonality_changepoints_dict': None}, custom={'fit_algorithm_dict': {'fit_algorithm': 'ridge', 'fit_algorithm_params': None}, 'feature_sets_enabled': 'auto', 'max_daily_seas_interaction_order': 5, 'max_weekly_seas_interaction_order': 2, 'extra_pred_cols': [], 'drop_pred_cols': None, 'explicit_pred_cols': None, 'min_admissible_value': None, 'max_admissible_value': None, 'regression_weight_col': None, 'normalize_method': None}, events={'holidays_to_model_separately': ("New Year's Day", 'Chinese New Year', 'Christmas Day', 'Independence Day', 'Thanksgiving', 'Labor Day', 'Good Friday', 'Easter Monday [England, Wales, Northern Ireland]', 'Memorial Day', 'Veterans Day'), 'holiday_lookup_countries': ('UnitedStates', 'UnitedKingdom', 'India', 'France', 'China'), 'holiday_pre_num_days': 2, 'holiday_post_num_days': 2, 'holiday_pre_post_num_dict': None, 'daily_event_df_dict': None}, growth={'growth_term': 'linear'}, hyperparameter_override=None, regressors={'regressor_cols': []}, lagged_regressors={'lagged_regressor_dict': None}, seasonality={'yearly_seasonality': 8, 'quarterly_seasonality': 0, 'monthly_seasonality': 7, 'weekly_seasonality': 1, 'daily_seasonality': 0}, uncertainty={'uncertainty_dict': None}), SILVERKITE_DAILY_1_CONFIG_2=ModelComponentsParam(autoregression={'autoreg_dict': 'auto', 'simulation_num': 10}, changepoints={'changepoints_dict': {'method': 'auto', 'resample_freq': '7D', 'regularization_strength': 0.624, 'potential_changepoint_distance': '7D', 'no_changepoint_distance_from_end': '17D', 'yearly_seasonality_order': 1, 'yearly_seasonality_change_freq': None}, 'seasonality_changepoints_dict': None}, custom={'fit_algorithm_dict': {'fit_algorithm': 'ridge', 'fit_algorithm_params': None}, 'feature_sets_enabled': 'auto', 'max_daily_seas_interaction_order': 5, 'max_weekly_seas_interaction_order': 2, 'extra_pred_cols': [], 'drop_pred_cols': None, 'explicit_pred_cols': None, 'min_admissible_value': None, 'max_admissible_value': None, 'regression_weight_col': None, 'normalize_method': None}, events={'holidays_to_model_separately': ("New Year's Day", 'Chinese New Year', 'Christmas Day', 'Independence Day', 'Thanksgiving', 'Labor Day', 'Good Friday', 'Easter Monday [England, Wales, Northern Ireland]', 'Memorial Day', 'Veterans Day'), 'holiday_lookup_countries': ('UnitedStates', 'UnitedKingdom', 'India', 'France', 'China'), 'holiday_pre_num_days': 2, 'holiday_post_num_days': 2, 'holiday_pre_post_num_dict': None, 'daily_event_df_dict': None}, growth={'growth_term': 'linear'}, hyperparameter_override=None, regressors={'regressor_cols': []}, lagged_regressors={'lagged_regressor_dict': None}, seasonality={'yearly_seasonality': 1, 'quarterly_seasonality': 0, 'monthly_seasonality': 4, 'weekly_seasonality': 6, 'daily_seasonality': 0}, uncertainty={'uncertainty_dict': None}), SILVERKITE_DAILY_1_CONFIG_3=ModelComponentsParam(autoregression={'autoreg_dict': 'auto', 'simulation_num': 10}, changepoints={'changepoints_dict': {'method': 'auto', 'resample_freq': '7D', 'regularization_strength': 0.59, 'potential_changepoint_distance': '7D', 'no_changepoint_distance_from_end': '8D', 'yearly_seasonality_order': 40, 'yearly_seasonality_change_freq': None}, 'seasonality_changepoints_dict': None}, custom={'fit_algorithm_dict': {'fit_algorithm': 'ridge', 'fit_algorithm_params': None}, 'feature_sets_enabled': 'auto', 'max_daily_seas_interaction_order': 5, 'max_weekly_seas_interaction_order': 2, 'extra_pred_cols': [], 'drop_pred_cols': None, 'explicit_pred_cols': None, 'min_admissible_value': None, 'max_admissible_value': None, 'regression_weight_col': None, 'normalize_method': None}, events={'holidays_to_model_separately': ("New Year's Day", 'Chinese New Year', 'Christmas Day', 'Independence Day', 'Thanksgiving', 'Labor Day', 'Good Friday', 'Easter Monday [England, Wales, Northern Ireland]', 'Memorial Day', 'Veterans Day'), 'holiday_lookup_countries': ('UnitedStates', 'UnitedKingdom', 'India', 'France', 'China'), 'holiday_pre_num_days': 2, 'holiday_post_num_days': 2, 'holiday_pre_post_num_dict': None, 'daily_event_df_dict': None}, growth={'growth_term': 'linear'}, hyperparameter_override=None, regressors={'regressor_cols': []}, lagged_regressors={'lagged_regressor_dict': None}, seasonality={'yearly_seasonality': 40, 'quarterly_seasonality': 0, 'monthly_seasonality': 0, 'weekly_seasonality': 2, 'daily_seasonality': 0}, uncertainty={'uncertainty_dict': None}), SILVERKITE_COMPONENT_KEYWORDS=<enum 'SILVERKITE_COMPONENT_KEYWORDS'>, SILVERKITE_EMPTY='DAILY_SEAS_NONE_GR_NONE_CP_NONE_HOL_NONE_FEASET_OFF_ALGO_LINEAR_AR_OFF_DSI_OFF_WSI_OFF', VALID_FREQ=['HOURLY', 'DAILY', 'WEEKLY'], SimpleSilverkiteTemplateOptions=<class 'greykite.framework.templates.simple_silverkite_template_config.SimpleSilverkiteTemplateOptions'>), estimator: greykite.sklearn.estimator.base_forecast_estimator.BaseForecastEstimator = SimpleSilverkiteEstimator())[source]¶

A template for SimpleSilverkiteEstimator.

Takes input data and optional configuration parameters to customize the model. Returns a set of parameters to call forecast_pipeline.

Notes

The attributes of a ForecastConfig for SimpleSilverkiteEstimator are:

computation_param: ComputationParam or None, default None
How to compute the result. See ComputationParam.

coverage: float or None, default None
Intended coverage of the prediction bands (0.0 to 1.0). Same as coverage in forecast_pipeline. You may tune how the uncertainty is computed via model_components.uncertainty[“uncertainty_dict”].

evaluation_metric_param: EvaluationMetricParam or None, default None
What metrics to evaluate. See EvaluationMetricParam.

evaluation_period_param: EvaluationPeriodParam or None, default None
How to split data for evaluation. See EvaluationPeriodParam.

forecast_horizon: int or None, default None
Number of periods to forecast into the future. Must be > 0 If None, default is determined from input data frequency Same as forecast_horizon in forecast_pipeline

metadata_param: MetadataParam or None, default None
Information about the input data. See MetadataParam.

model_components_param: ModelComponentsParam, list [ModelComponentsParam] or None, default None
Parameters to tune the model. See ModelComponentsParam. The fields are dictionaries with the following items.

See inline comments on which values accept lists for grid search.
seasonality: dict [str, any] or None, optional
Seasonality configuration dictionary, with the following optional keys. (keys are SilverkiteSeasonalityEnum members in lower case).

The keys are parameters of forecast_simple_silverkite. Refer to that function for more details.

"yearly_seasonality": str or bool or int or a list of such values for grid search, default ‘auto’
Determines the yearly seasonality ‘auto’, True, False, or a number for the Fourier order

"quarterly_seasonality": str or bool or int or a list of such values for grid search, default ‘auto’
Determines the quarterly seasonality ‘auto’, True, False, or a number for the Fourier order

"monthly_seasonality": str or bool or int or a list of such values for grid search, default ‘auto’
Determines the monthly seasonality ‘auto’, True, False, or a number for the Fourier order

"weekly_seasonality": str or bool or int or a list of such values for grid search, default ‘auto’
Determines the weekly seasonality ‘auto’, True, False, or a number for the Fourier order

"daily_seasonality": str or bool or int or a list of such values for grid search, default ‘auto’
Determines the daily seasonality ‘auto’, True, False, or a number for the Fourier order

growth: dict [str, any] or None, optional
Growth configuration dictionary with the following optional key:

"growth_term": str or None or a list of such values for grid search
How to model the growth. Valid options are “linear”, “quadratic”, “sqrt”, “cubic”, “cuberoot” All these terms have their origin at the train start date.

events: dict [str, any] or None, optional
Holiday/events configuration dictionary with the following optional keys:

"holiday_lookup_countries": list [str] or “auto” or None or a list of such values for grid search, default “auto”
The countries that contain the holidays you intend to model (holidays_to_model_separately).

If “auto”, uses a default list of countries that contain the default holidays_to_model_separately. See HOLIDAY_LOOKUP_COUNTRIES_AUTO.

If a list, must be a list of country names.

If None or an empty list, no holidays are modeled.

"holidays_to_model_separately": list [str] or “auto” or ALL_HOLIDAYS_IN_COUNTRIES or None or a list of such values for grid search, default “auto” # noqa: E501
Which holidays to include in the model. The model creates a separate key, value for each item in holidays_to_model_separately. The other holidays in the countries are grouped together as a single effect.

If “auto”, uses a default list of important holidays. See HOLIDAYS_TO_MODEL_SEPARATELY_AUTO.

If ALL_HOLIDAYS_IN_COUNTRIES, uses all available holidays in holiday_lookup_countries. This can often create a model that has too many parameters, and should typically be avoided.

If a list, must be a list of holiday names.

If None or an empty list, all holidays in holiday_lookup_countries are grouped together as a single effect.

Use holiday_lookup_countries to provide a list of countries where these holiday occur.

"holiday_pre_num_days": int or a list of such values for grid search, default 2
model holiday effects for pre_num days before the holiday. The unit is days, not periods. It does not depend on input data frequency.

"holiday_post_num_days": int or a list of such values for grid search, default 2
model holiday effects for post_num days after the holiday. The unit is days, not periods. It does not depend on input data frequency.

"holiday_pre_post_num_dict": dict [str, (int, int)] or None, default None
Overrides pre_num and post_num for each holiday in holidays_to_model_separately. For example, if holidays_to_model_separately contains “Thanksgiving” and “Labor Day”, this parameter can be set to {"Thanksgiving": [1, 3], "Labor Day": [1, 2]}, denoting that the “Thanksgiving” pre_num is 1 and post_num is 3, and “Labor Day” pre_num is 1 and post_num is 2. Holidays not specified use the default given by pre_num and post_num.

"daily_event_df_dict": dict [str, pandas.DataFrame] or None, default None
A dictionary of data frames, each representing events data for the corresponding key. Specifies additional events to include besides the holidays specified above. The format is the same as in forecast. The DataFrame has two columns:

The first column contains event dates. Must be in a format recognized by pandas.to_datetime. Must be at daily frequency for proper join. It is joined against the time in df, converted to a day: pd.to_datetime(pd.DatetimeIndex(df[time_col]).date).

the second column contains the event label for each date

The column order is important; column names are ignored. The event dates must span their occurrences in both the training and future prediction period.

During modeling, each key in the dictionary is mapped to a categorical variable named f"{EVENT_PREFIX}_{key}", whose value at each timestamp is specified by the corresponding DataFrame.

For example, to manually specify a yearly event on September 1 during a training/forecast period that spans 2020-2022:
daily_event_df_dict = {
    "custom_event": pd.DataFrame({
        "date": ["2020-09-01", "2021-09-01", "2022-09-01"],
        "label": ["is_event", "is_event", "is_event"]
    })
}
It’s possible to specify multiple events in the same df. Two events, "sep" and "oct" are specified below for 2020-2021:
daily_event_df_dict = {
    "custom_event": pd.DataFrame({
        "date": ["2020-09-01", "2020-10-01", "2021-09-01", "2021-10-01"],
        "event_name": ["sep", "oct", "sep", "oct"]
    })
}
Use multiple keys if two events may fall on the same date. These events must be in separate DataFrames:
daily_event_df_dict = {
    "fixed_event": pd.DataFrame({
        "date": ["2020-09-01", "2021-09-01", "2022-09-01"],
        "event_name": "fixed_event"
    }),
    "moving_event": pd.DataFrame({
        "date": ["2020-09-01", "2021-08-28", "2022-09-03"],
        "event_name": "moving_event"
    }),
}
The multiple event specification can be used even if events never overlap. An equivalent specification to the second example:
daily_event_df_dict = {
    "sep": pd.DataFrame({
        "date": ["2020-09-01", "2021-09-01"],
        "event_name": "is_event"
    }),
    "oct": pd.DataFrame({
        "date": ["2020-10-01", "2021-10-01"],
        "event_name": "is_event"
    }),
}
Note: All these events are automatically added to the model. There is no need to specify them in extra_pred_cols as you would for forecast.

Note: Do not use EVENT_DEFAULT in the second column. This is reserved to indicate dates that do not correspond to an event.
changepoints: dict [str, dict] or None, optional
Specifies the changepoint configuration. Dictionary with the following optional key:

"changepoints_dict": dict or None or a list of such values for grid search
Changepoints dictionary passed to forecast_simple_silverkite. A dictionary with the following optional keys:

"method": str
The method to locate changepoints. Valid options:

“uniform”. Places n_changepoints evenly spaced changepoints to allow growth to change.

“custom”. Places changepoints at the specified dates.

“auto”. Automatically detects change points.

Additional keys to provide parameters for each particular method are described below.

"continuous_time_col": str or None
Column to apply growth_func to, to generate changepoint features Typically, this should match the growth term in the model

"growth_func": callable or None
Growth function (numeric -> numeric). Changepoint features are created by applying growth_func to “continuous_time_col” with offsets. If None, uses identity function to use continuous_time_col directly as growth term

If changepoints_dict[“method”] == “uniform”, this other key is required:

"n_changepoints": int
number of changepoints to evenly space across training period

If changepoints_dict[“method”] == “custom”, this other key is required:

"dates": list [int or float or str or datetime]
Changepoint dates. Must be parsable by pd.to_datetime. Changepoints are set at the closest time on or after these dates in the dataset.

If changepoints_dict[“method”] == “auto”, optional keys can be passed that match the parameters in find_trend_changepoints (except df, time_col and value_col, which are already known). To add manually specified changepoints to the automatically detected ones, the keys dates, combine_changepoint_min_distance and keep_detected can be specified, which correspond to the three parameters custom_changepoint_dates, min_distance and keep_detected in combine_detected_and_custom_trend_changepoints.

"seasonality_changepoints_dict": dict or None or a list of such values for grid search
seasonality changepoints dictionary passed to forecast_simple_silverkite. The optional keys are the parameters in find_seasonality_changepoints. You don’t need to provide df, time_col, value_col or trend_changepoints, since they are passed with the class automatically.

autoregression: dict [str, dict] or None, optional
Specifies the autoregression configuration. Dictionary with the following optional keys:

"autoreg_dict": dict or str or None or a list of such values for grid search
If a dict: A dictionary with arguments for build_autoreg_df. That function’s parameter value_col is inferred from the input of current function self.forecast. Other keys are:

"lag_dict" : dict or None "agg_lag_dict" : dict or None "series_na_fill_func" : callable

If a str: The string will represent a method and a dictionary will be constructed using that str. Currently only implemented method is “auto” which uses __get_default_autoreg_dict to create a dictionary. See more details for above parameters in build_autoreg_df.

"simulation_num"int, default 10
The number of simulations to use. Applies only if any of the lags in autoreg_dict are smaller than forecast_horizon. In that case, simulations are needed to generate forecasts and prediction intervals.

regressors: dict [str, any] or None, optional
Specifies the regressors to include in the model (e.g. macro-economic factors). Dictionary with the following optional keys:

"regressor_cols": list [str] or None or a list of such values for grid search
The columns in df to use as regressors. Note that regressor values must be available in df for all prediction dates. Thus, df will contain timestamps for both training and future prediction.

regressors must be available on all dates

the response must be available for training dates (metadata[“value_col”])

Use extra_pred_cols to specify interactions of any model terms with the regressors.

lagged_regressors: dict [str, dict] or None, optional
Specifies the lagged regressors configuration. Dictionary with the following optional key:
"lagged_regressor_dict": dict or None or a list of such values for grid search
A dictionary with arguments for build_autoreg_df_multi. The keys of the dictionary are the target lagged regressor column names. It can leverage the regressors included in df. The value of each key is either a dict or str. If dict, it has the following keys:

"lag_dict" : dict or None "agg_lag_dict" : dict or None "series_na_fill_func" : callable

If str, it represents a method and a dictionary will be constructed using that str. Currently the only implemented method is “auto” which uses SilverkiteForecast’s __get_default_lagged_regressor_dict to create a dictionary for each lagged regressor. An example:
lagged_regressor_dict = {
    "regressor1": {
        "lag_dict": {"orders": [1, 2, 3]},
        "agg_lag_dict": {
            "orders_list": [[7, 7 * 2, 7 * 3]],
            "interval_list": [(8, 7 * 2)]},
        "series_na_fill_func": lambda s: s.bfill().ffill()},
    "regressor2": "auto"}
Check the docstring of build_autoreg_df_multi for more details for each argument.
uncertainty: dict [str, dict] or None, optional
Along with coverage, specifies the uncertainty interval configuration. Use coverage to set interval size. Use uncertainty to tune the calculation.

"uncertainty_dict": str or dict or None or a list of such values for grid search
“auto” or a dictionary on how to fit the uncertainty model. If a dictionary, valid keys are:

"uncertainty_method": str
The title of the method. Only "simple_conditional_residuals" is implemented in fit_ml_model which calculates intervals using residuals.

"params": dict
A dictionary of parameters needed for the requested uncertainty_method. For example, for uncertainty_method="simple_conditional_residuals", see parameters of conf_interval:

"conditional_cols"

"quantiles"

"quantile_estimation_method"

"sample_size_thresh"

"small_sample_size_method"

"small_sample_size_quantile"

The default value for quantiles is inferred from coverage.

If “auto”, see get_silverkite_uncertainty_dict for the default value. If coverage is not None and uncertainty_dict is not provided, then the “auto” setting is used.

If coverage is None and uncertainty_dict is None, then no intervals are returned.

custom: dict [str, any] or None, optional
Custom parameters that don’t fit the categories above. Dictionary with the following optional keys:

"fit_algorithm_dict": dict or a list of such values for grid search
How to fit the model. A dictionary with the following optional keys.

"fit_algorithm": str, optional, default “ridge”
The type of predictive model used in fitting.

See fit_model_via_design_matrix for available options and their parameters.

"fit_algorithm_params": dict or None, optional, default None
Parameters passed to the requested fit_algorithm. If None, uses the defaults in fit_model_via_design_matrix.

"feature_sets_enabled": dict [str, bool or “auto” or None] or bool or “auto” or None; or a list of such values for grid search
Whether to include interaction terms and categorical variables to increase model flexibility.

If a dict, boolean values indicate whether include various sets of features in the model. The following keys are recognized (from SilverkiteColumn):

"COLS_HOUR_OF_WEEK": str
Constant hour of week effect

"COLS_WEEKEND_SEAS": str
Daily seasonality interaction with is_weekend

"COLS_DAY_OF_WEEK_SEAS": str
Daily seasonality interaction with day of week

"COLS_TREND_DAILY_SEAS": str
Allow daily seasonality to change over time by is_weekend

"COLS_EVENT_SEAS": str
Allow sub-daily event effects

"COLS_EVENT_WEEKEND_SEAS": str
Allow sub-daily event effect to interact with is_weekend

"COLS_DAY_OF_WEEK": str
Constant day of week effect

"COLS_TREND_WEEKEND": str
Allow trend (growth, changepoints) to interact with is_weekend

"COLS_TREND_DAY_OF_WEEK": str
Allow trend to interact with day of week

"COLS_TREND_WEEKLY_SEAS": str
Allow weekly seasonality to change over time

The following dictionary values are recognized:

True: include the feature set in the model

False: do not include the feature set in the model

None: do not include the feature set in the model

“auto” or not provided: use the default setting based on data frequency and size

If not a dict:

if a boolean, equivalent to a dictionary with all values set to the boolean.

if None, equivalent to a dictionary with all values set to False.

if “auto”, equivalent to a dictionary with all values set to “auto”.

"max_daily_seas_interaction_order": int or None or a list of such values for grid search, default 5
Max fourier order to use for interactions with daily seasonality. (COLS_EVENT_SEAS, COLS_EVENT_WEEKEND_SEAS, COLS_WEEKEND_SEAS, COLS_DAY_OF_WEEK_SEAS, COLS_TREND_DAILY_SEAS).

Model includes interactions terms specified by feature_sets_enabled up to the order limited by this value and the available order from seasonality.

"max_weekly_seas_interaction_order"int or None or a list of such values for grid search, default 2
Max fourier order to use for interactions with weekly seasonality (COLS_TREND_WEEKLY_SEAS).

Model includes interactions terms specified by feature_sets_enabled up to the order limited by this value and the available order from seasonality.

"extra_pred_cols": list [str] or None or a list of such values for grid search, default None
Names of extra predictor columns to pass to forecast_silverkite. The standard interactions can be controlled via feature_sets_enabled parameter. Accepts any valid patsy model formula term. Can be used to model complex interactions of time features, events, seasonality, changepoints, regressors. Columns should be generated by build_silverkite_features or included with input data. These are added to any features already included by feature_sets_enabled and terms specified by model.

"drop_pred_cols"list [str] or None, default None
Names of predictor columns to be dropped from the final model. Ignored if None.

"explicit_pred_cols"list [str] or None, default None
Names of the explicit predictor columns which will be the only variables in the final model. Note that this overwrites the generated predictors in the model and may include new terms not appearing in the predictors (e.g. interaction terms). Ignored if None.

"min_admissible_value": float or double or int or None, default None
The lowest admissible value for the forecasts and prediction intervals. Any value below this will be mapped back to this value. If None, there is no lower bound.

"max_admissible_value": float or double or int or None, default None
The highest admissible value for the forecasts and prediction intervals. Any value above this will be mapped back to this value. If None, there is no upper bound.

"normalize_method": str or None, default None
The normalization method for feature matrix. Available values are “statistical” and “min_max”.

hyperparameter_override: dict [str, any] or None or list [dict [str, any] or None], optional
After the above model components are used to create a hyperparameter grid, the result is updated by this dictionary, to create new keys or override existing ones. Allows for complete customization of the grid search.

Keys should have format {named_step}__{parameter_name} for the named steps of the sklearn.pipeline.Pipeline returned by this function. See sklearn.pipeline.Pipeline.

For example:
hyperparameter_override={
    "estimator__silverkite": SimpleSilverkiteForecast(),
    "estimator__silverkite_diagnostics": SilverkiteDiagnostics(),
    "estimator__growth_term": "linear",
    "input__response__null__impute_algorithm": "ts_interpolate",
    "input__response__null__impute_params": {"orders": [7, 14]},
    "input__regressors_numeric__normalize__normalize_algorithm": "RobustScaler",
}
If a list of dictionaries, grid search will be done for each dictionary in the list. Each dictionary in the list override the defaults. This enables grid search over specific combinations of parameters to reduce the search space.

For example, the first dictionary could define combinations of parameters for a “complex” model, and the second dictionary could define combinations of parameters for a “simple” model, to prevent mixed combinations of simple and complex.

Or the first dictionary could grid search over fit algorithm, and the second dictionary could use a single fit algorithm and grid search over seasonality.

The result is passed as the param_distributions parameter to sklearn.model_selection.RandomizedSearchCV.
model_template: str, list`[`str] or None, default None
The simple silverkite template support single templates, multi templates or a list of single/multi templates. A valid single template must be either SILVERKITE or consists of

{FREQ}_SEAS_{VAL}_GR_{VAL}_CP_{VAL}_HOL_{VAL}_FEASET_{VAL}_ALGO_{VAL}_AR_{VAL}

For example, we have DAILY_SEAS_NM_GR_LINEAR_CP_LT_HOL_NONE_FEASET_ON_ALGO_RIDGE_AR_ON. The valid FREQ and VAL can be found at template_defaults. The components stand for seasonality, growth, changepoints_dict, events, feature_sets_enabled, fit_algorithm and autoregression in ModelComponentsParam, which is used in SimpleSilverkiteTemplate. Users are allowed to

Omit any number of component-value pairs, and the omitted will be filled with default values.

Switch the order of different component-value pairs.

A valid multi template must belong to MULTI_TEMPLATES or must be a list of single or multi template names.

DEFAULT_MODEL_TEMPLATE = 'SILVERKITE'¶: The default model template. See ModelTemplateEnum. Uses a string to avoid circular imports. Overrides the value from ForecastConfigDefaults.

property allow_model_template_list¶: SimpleSilverkiteTemplate allows config.model_template to be a list.

property allow_model_components_param_list¶: SilverkiteTemplate allows config.model_components_param to be a list.

property constants¶: Constants used by the template class. Includes the model templates and their default values.

get_regressor_cols()[source]¶

Returns regressor column names from the model components.

Implements the method in BaseTemplate.

Uses these attributes:

model_components: ModelComponentsParam, list [ModelComponentsParam] or None, default None

Configuration of model growth, seasonality, holidays, etc. See SimpleSilverkiteTemplate for details.

Returns: regressor_cols – The names of regressor columns used in any hyperparameter set requested by model_components. None if there are no regressors.
Return type: list [str] or None

get_lagged_regressor_info()[source]¶

Returns lagged regressor column names and minimal/maximal lag order. The lag order can be used to check potential imputation in the computation of lags.

Implements the method in BaseTemplate.

Returns

lagged_regressor_info – A dictionary that includes the lagged regressor column names and maximal/minimal lag order The keys are:

lagged_regressor_colslist [str] or None
See forecast_pipeline.

overall_min_lag_order : int or None overall_max_lag_order : int or None

For example:

self.config.model_components_param.lagged_regressors["lagged_regressor_dict"] = [
    {"regressor1": {
        "lag_dict": {"orders": [7]},
        "agg_lag_dict": {
            "orders_list": [[7, 7 * 2, 7 * 3]],
            "interval_list": [(8, 7 * 2)]},
        "series_na_fill_func": lambda s: s.bfill().ffill()}
    },
    {"regressor2": {
        "lag_dict": {"orders": [2]},
        "agg_lag_dict": {
            "orders_list": [[7, 7 * 2]],
            "interval_list": [(8, 7 * 2)]},
        "series_na_fill_func": lambda s: s.bfill().ffill()}
    },
    {"regressor3": "auto"}
]

Then the function returns:

lagged_regressor_info = {
    "lagged_regressor_cols": ["regressor1", "regressor2", "regressor3"],
    "overall_min_lag_order": 2,
    "overall_max_lag_order": 21
}

Note that “regressor3” is skipped as the “auto” option makes sure the lag order is proper.

Return type

dict

get_hyperparameter_grid()[source]¶

Returns hyperparameter grid.

Implements the method in BaseTemplate.

Converts model components, time properties, and model template into SimpleSilverkiteEstimator hyperparameters.

Uses these attributes:

model_components: ModelComponentsParam, list [ModelComponentsParam] or None, default None

Configuration of model growth, seasonality, events, etc. See SimpleSilverkiteTemplate for details.

time_properties: dict [str, any] or None, default None
Time properties dictionary (likely produced by get_forecast_time_properties) with keys:

"period": int
Period of each observation (i.e. minimum time between observations, in seconds).

"simple_freq": SimpleTimeFrequencyEnum
SimpleTimeFrequencyEnum member corresponding to data frequency.

"num_training_points": int
Number of observations for training.

"num_training_days": int
Number of days for training.

"start_year": int
Start year of the training period.

"end_year": int
End year of the forecast period.

"origin_for_time_vars": float
Continuous time representation of the first date in df.

model_template: str, default “SILVERKITE”
The name of model template, must be one of the valid templates defined in SimpleSilverkiteTemplate.

Notes

forecast_pipeline handles the train/test splits according to EvaluationPeriodParam, so estimator__train_test_thresh and estimator__training_fraction are always None.

Similarly, estimator__origin_for_time_vars is set to None.

Returns: hyperparameter_grid – hyperparameter_grid for grid search in forecast_pipeline. The output dictionary values are lists, combined in grid search.
Return type: dict [str, list [any]] or list [ dict [str, list [any]] ]

check_template_type(template)[source]¶

Checks the template name is valid and whether it is single or multi template. Raises an error if the template is not recognized.

A valid single template must be either SILVERKITE or consists of

{FREQ}_SEAS_{VAL}_GR_{VAL}_CP_{VAL}_HOL_{VAL}_FEASET_{VAL}_ALGO_{VAL}_AR_{VAL}

For example, we have DAILY_SEAS_NM_GR_LINEAR_CP_LT_HOL_NONE_FEASET_ON_ALGO_RIDGE_AR_ON. The valid FREQ and VAL can be found at template_defaults. The components stand for seasonality, growth, changepoints_dict, events, feature_sets_enabled, fit_algorithm and autoregression in ModelComponentsParam, which is used in SimpleSilverkiteTemplate. Users are allowed to

Omit any number of component-value pairs, and the omitted will be filled with default values.

Switch the order of different component-value pairs.

A valid multi template must belong to MULTI_TEMPLATES or must be a list of single or multi template names.

Parameters: template (str, SimpleSilverkiteTemplateName or list`[`str, SimpleSilverkiteTemplateName]) – The model_template parameter fed into ForecastConfig. for simple silverkite templates.
Returns: template_type – “single” or “multi”.
Return type: str

get_model_components_from_model_template(template)[source]¶

Gets the ModelComponentsParam class from model template.

The template could be a name string, a SimpleSilverkiteTemplateOptions dataclass, or a list of such strings and/or dataclasses. If a list is given, a list of ModelComponentsParam is returned. If a single element is given, a list of length 1 is returned.

Parameters: template (str, SimpleSilverkiteTemplateOptions or list [str, SimpleSilverkiteTemplateOptions]) – The model_template in ForecastConfig, could be a name string, a SimpleSilverkiteTemplateOptions dataclass, or a list of such strings and/or dataclasses.
Returns: model_components_param – The list of ModelComponentsParam class(es) that correspond to template.
Return type: list [ModelComponentsParam]

static apply_computation_defaults(computation: Optional[greykite.framework.templates.autogen.forecast_config.ComputationParam] = None) → greykite.framework.templates.autogen.forecast_config.ComputationParam ¶

Applies the default ComputationParam values to the given object. If an expected attribute value is provided, the value is unchanged. Otherwise the default value for it is used. Other attributes are untouched. If the input object is None, it creates a ComputationParam object.

Parameters: computation (ComputationParam or None) – The ComputationParam object.
Returns: computation – Valid ComputationParam object with the provided attribute values and the default attribute values if not.
Return type: ComputationParam

static apply_evaluation_metric_defaults(evaluation: Optional[greykite.framework.templates.autogen.forecast_config.EvaluationMetricParam] = None) → greykite.framework.templates.autogen.forecast_config.EvaluationMetricParam ¶

Applies the default EvaluationMetricParam values to the given object. If an expected attribute value is provided, the value is unchanged. Otherwise the default value for it is used. Other attributes are untouched. If the input object is None, it creates a EvaluationMetricParam object.

Parameters: evaluation (EvaluationMetricParam or None) – The EvaluationMetricParam object.
Returns: evaluation – Valid EvaluationMetricParam object with the provided attribute values and the default attribute values if not.
Return type: EvaluationMetricParam

static apply_evaluation_period_defaults(evaluation: Optional[greykite.framework.templates.autogen.forecast_config.EvaluationPeriodParam] = None) → greykite.framework.templates.autogen.forecast_config.EvaluationPeriodParam ¶

Applies the default EvaluationPeriodParam values to the given object. If an expected attribute value is provided, the value is unchanged. Otherwise the default value for it is used. Other attributes are untouched. If the input object is None, it creates a EvaluationPeriodParam object.

Parameters: evaluation (EvaluationPeriodParam or None) – The EvaluationMetricParam object.
Returns: evaluation – Valid EvaluationPeriodParam object with the provided attribute values and the default attribute values if not.
Return type: EvaluationPeriodParam

apply_forecast_config_defaults(config: Optional[greykite.framework.templates.autogen.forecast_config.ForecastConfig] = None) → greykite.framework.templates.autogen.forecast_config.ForecastConfig ¶

Applies the default Forecast Config values to the given config. If an expected attribute value is provided, the value is unchanged. Otherwise the default value for it is used. Other attributes are untouched. If the input config is None, it creates a Forecast Config.

Parameters: config (ForecastConfig or None) – Forecast configuration if available. See ForecastConfig.
Returns: config – A valid Forecast Config which contains the provided attribute values and the default attribute values if not.
Return type: ForecastConfig

static apply_metadata_defaults(metadata: Optional[greykite.framework.templates.autogen.forecast_config.MetadataParam] = None) → greykite.framework.templates.autogen.forecast_config.MetadataParam ¶

Applies the default MetadataParam values to the given object. If an expected attribute value is provided, the value is unchanged. Otherwise the default value for it is used. Other attributes are untouched. If the input object is None, it creates a MetadataParam object.

Parameters: metadata (MetadataParam or None) – The MetadataParam object.
Returns: metadata – Valid MetadataParam object with the provided attribute values and the default attribute values if not.
Return type: MetadataParam

static apply_model_components_defaults(model_components: Optional[Union[greykite.framework.templates.autogen.forecast_config.ModelComponentsParam, List[Optional[greykite.framework.templates.autogen.forecast_config.ModelComponentsParam]]]] = None) → Union[greykite.framework.templates.autogen.forecast_config.ModelComponentsParam, List[greykite.framework.templates.autogen.forecast_config.ModelComponentsParam]]¶

Applies the default ModelComponentsParam values to the given object.

Converts None to a ModelComponentsParam object. Unpacks a list of a single element to the element itself.

Parameters: model_components (ModelComponentsParam or None or list of such items) – The ModelComponentsParam object.
Returns: model_components – Valid ModelComponentsParam object with the provided attribute values and the default attribute values if not.
Return type: ModelComponentsParam or list of such items

apply_model_template_defaults(model_template: Optional[Union[str, List[Optional[str]]]] = None) → Union[str, List[str]]¶

Applies the default ModelComponentsParam values to the given object.

Unpacks a list of a single element to the element itself. Sets default value if None.

Parameters: model_template (str or None or list [None, str]) – The model template name. See valid names in ModelTemplateEnum.
Returns: model_template – The model template name, with defaults value used if not provided.
Return type: str or list [str]

static apply_template_decorator(func)¶

Decorator for apply_template_for_pipeline_params function.

By default, this applies apply_forecast_config_defaults to config.

Subclass may override this for pre/post processing of apply_template_for_pipeline_params, such as input validation. In this case, apply_template_for_pipeline_params must also be implemented in the subclass.

apply_template_for_pipeline_params(df: pandas.core.frame.DataFrame, config: Optional[greykite.framework.templates.autogen.forecast_config.ForecastConfig] = None) → Dict¶

Implements template interface method. Takes input data and optional configuration parameters to customize the model. Returns a set of parameters to call forecast_pipeline.

See template interface for parameters and return value.

Uses the methods in this class to set:

"regressor_cols" : get_regressor_cols()

lagged_regressor_cols : get_lagged_regressor_info()

"pipeline" : get_pipeline()

"time_properties" : get_forecast_time_properties()

"hyperparameter_grid" : get_hyperparameter_grid()

All other parameters are taken directly from config.

property estimator¶: The estimator instance to use as the final step in the pipeline. An instance of greykite.sklearn.estimator.base_forecast_estimator.BaseForecastEstimator.

get_forecast_time_properties()¶

Returns forecast time parameters.

Uses self.df, self.config, self.regressor_cols.

Available parameters:

self.df

self.config

self.score_func

self.score_func_greater_is_better

self.regressor_cols

self.lagged_regressor_cols

self.estimator

self.pipeline

Returns

time_properties – Time properties dictionary (likely produced by get_forecast_time_properties) with keys:

"period"int: Period of each observation (i.e. minimum time between observations, in seconds).
"simple_freq"SimpleTimeFrequencyEnum: SimpleTimeFrequencyEnum member corresponding to data frequency.
"num_training_points"int: Number of observations for training.
"num_training_days"int: Number of days for training.
"start_year"int: Start year of the training period.
"end_year"int: End year of the forecast period.
"origin_for_time_vars"float: Continuous time representation of the first date in df.

Return type

dict [str, any] or None, default None

get_pipeline()¶

Returns pipeline.

Implementation may be overridden by subclass if a different pipeline is desired.

Uses self.estimator, self.score_func, self.score_func_greater_is_better, self.config, self.regressor_cols.

Available parameters:

self.df

self.config

self.score_func

self.score_func_greater_is_better

self.regressor_cols

self.estimator

Returns: pipeline – See forecast_pipeline.
Return type: sklearn.pipeline.Pipeline

class greykite.sklearn.estimator.simple_silverkite_estimator.SimpleSilverkiteEstimator(silverkite: greykite.algo.forecast.silverkite.forecast_simple_silverkite.SimpleSilverkiteForecast = <greykite.algo.forecast.silverkite.forecast_simple_silverkite.SimpleSilverkiteForecast object>, silverkite_diagnostics: greykite.algo.forecast.silverkite.silverkite_diagnostics.SilverkiteDiagnostics = <greykite.algo.forecast.silverkite.silverkite_diagnostics.SilverkiteDiagnostics object>, score_func: callable = <function mean_squared_error>, coverage: float = None, null_model_params: Optional[Dict] = None, time_properties: Optional[Dict] = None, freq: Optional[str] = None, forecast_horizon: Optional[int] = None, origin_for_time_vars: Optional[float] = None, train_test_thresh: Optional[datetime.datetime] = None, training_fraction: Optional[float] = None, fit_algorithm_dict: Optional[Dict] = None, holidays_to_model_separately: Optional[Union[str, List[str]]] = 'auto', holiday_lookup_countries: Optional[Union[str, List[str]]] = 'auto', holiday_pre_num_days: int = 2, holiday_post_num_days: int = 2, holiday_pre_post_num_dict: Optional[Dict] = None, daily_event_df_dict: Optional[Dict] = None, changepoints_dict: Optional[Dict] = None, yearly_seasonality: Union[bool, str, int] = 'auto', quarterly_seasonality: Union[bool, str, int] = 'auto', monthly_seasonality: Union[bool, str, int] = 'auto', weekly_seasonality: Union[bool, str, int] = 'auto', daily_seasonality: Union[bool, str, int] = 'auto', max_daily_seas_interaction_order: Optional[int] = None, max_weekly_seas_interaction_order: Optional[int] = None, autoreg_dict: Optional[Dict] = None, past_df: Optional[pandas.core.frame.DataFrame] = None, lagged_regressor_dict: Optional[Dict] = None, seasonality_changepoints_dict: Optional[Dict] = None, min_admissible_value: Optional[float] = None, max_admissible_value: Optional[float] = None, uncertainty_dict: Optional[Dict] = None, normalize_method: Optional[str] = None, growth_term: Optional[str] = 'linear', regressor_cols: Optional[List[str]] = None, feature_sets_enabled: Optional[Union[bool, Dict[str, bool]]] = None, extra_pred_cols: Optional[List[str]] = None, drop_pred_cols: Optional[List[str]] = None, explicit_pred_cols: Optional[List[str]] = None, regression_weight_col: Optional[str] = None, simulation_based: Optional[bool] = False, simulation_num: int = 10)[source]¶

Wrapper for forecast_simple_silverkite.

Parameters

score_func (callable, optional, default mean_squared_error) – See BaseForecastEstimator.
coverage (float between [0.0, 1.0] or None, optional) – See BaseForecastEstimator.
null_model_params (dict or None, optional) – Dictionary with arguments to define DummyRegressor null model, default is None. See BaseForecastEstimator.
fit_algorithm_dict (dict or None, optional) –
How to fit the model. A dictionary with the following optional keys.

"fit_algorithm"str, optional, default “ridge”
The type of predictive model used in fitting.

See fit_model_via_design_matrix for available options and their parameters.

"fit_algorithm_params"dict or None, optional, default None
Parameters passed to the requested fit_algorithm. If None, uses the defaults in fit_model_via_design_matrix.
uncertainty_dict (dict or str or None, optional) – How to fit the uncertainty model. See forecast. Note that this is allowed to be “auto”. If None or “auto”, will be set to a default value by coverage before calling forecast_silverkite. See BaseForecastEstimator for details.
kwargs (additional parameters) –
Other parameters are the same as in forecast_simple_silverkite.

See source code __init__ for the parameter names, and refer to forecast_simple_silverkite for their description.

If this Estimator is called from forecast_pipeline, train_test_thresh and training_fraction should almost always be None, because train/test is handled outside this Estimator.

Notes

Attributes match those of BaseSilverkiteEstimator.

See also

None: For attributes and details on fit, predict, and component plots.
None: Function to transform the parameters to call forecast_silverkite fit.
None: Functions performing the fit and predict.

fit(X, y=None, time_col='ts', value_col='y', **fit_params)[source]¶

Fits Silverkite forecast model.

Parameters

X (pandas.DataFrame) – Input timeseries, with timestamp column, value column, and any additional regressors. The value column is the response, included in X to allow transformation by sklearn.pipeline.
y (ignored) – The original timeseries values, ignored. (The y for fitting is included in X).
time_col (str) – Time column name in X.
value_col (str) – Value column name in X.
fit_params (dict) – additional parameters for null model.

Returns

self – Fitted model is stored in self.model_dict.

Return type

self

finish_fit()¶

Makes important values of self.model_dict conveniently accessible.

To be called by subclasses at the end of their fit method. Sets {pred_cols, feature_cols, and coef_}.

fit_uncertainty(df: pandas.core.frame.DataFrame, uncertainty_dict: dict, **kwargs)¶

Fits the uncertainty model with a given df and uncertainty_dict.

Parameters

df (pandas.DataFrame) – A dataframe representing the data to fit the uncertainty model.
uncertainty_dict (dict [str, any]) –
The uncertainty model specification. It should have the following keys:

”uncertainty_method”: a string that is in
UncertaintyMethodEnum.

”params”: a dictionary that includes any additional parameters needed by the uncertainty method.
kwargs (additional parameters to be fed into the uncertainty method.) – These parameters are from the estimator attributes, not given by user.

Returns

Return type

The function sets self.uncertainty_model and does not return anything.

get_max_ar_order()¶

Gets the maximum autoregression order.

Returns: max_ar_order – The maximum autoregression order.
Return type: int

get_params(deep=True)¶

Get parameters for this estimator.

Parameters: deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns: params – Parameter names mapped to their values.
Return type: dict

plot_seasonalities(title=None)¶

Convenience function to plot the data and the seasonality components.

Parameters: title (str, optional, default None) – Plot title.
Returns: fig – Figure.
Return type: plotly.graph_objects.Figure

plot_trend(title=None)¶

Convenience function to plot the data and the trend component.

Parameters: title (str, optional, default None) – Plot title.
Returns: fig – Figure.
Return type: plotly.graph_objects.Figure

plot_trend_changepoint_detection(params=None)¶

Convenience function to plot the original trend changepoint detection results.

Parameters

params (dict or None, default None) –

The parameters in plot. If set to None, all components will be plotted.

Note: seasonality components plotting is not supported currently. plot parameter must be False.

Returns

fig – Figure.

Return type

plotly.graph_objects.Figure

property pred_category¶

A dictionary that stores the predictor names in each category.

This property is not initialized until used. This speeds up the fitting process. The categories includes

“intercept” : the intercept.

“time_features” : the predictors that include TIME_FEATURES but not SEASONALITY_REGEX.

“event_features” : the predictors that include EVENT_PREFIX.

“trend_features” : the predictors that include TREND_REGEX but not SEASONALITY_REGEX.

“seasonality_features” : the predictors that include SEASONALITY_REGEX.

“lag_features” : the predictors that include LAG_REGEX.

“regressor_features” : external regressors and other predictors manually passed to extra_pred_cols, but not in the categories above.

“interaction_features” : the predictors that include interaction terms, i.e., including a colon.

Note that each predictor falls into at least one category. Some “time_features” may also be “trend_features”. Predictors with an interaction are classified into all categories matched by the interaction components. Thus, “interaction_features” are already included in the other categories.

predict(X, y=None)¶

Creates forecast for the dates specified in X.

Parameters

X (pandas.DataFrame) – Input timeseries with timestamp column and any additional regressors. Timestamps are the dates for prediction. Value column, if provided in X, is ignored.
y (ignored.) –

Returns

predictions –

Forecasted values for the dates in X. Columns:

TIME_COL: dates

PREDICTED_COL: predictions

PREDICTED_LOWER_COL: lower bound of predictions, optional

PREDICTED_UPPER_COL: upper bound of predictions, optional

[other columns], optional

PREDICTED_LOWER_COL and PREDICTED_UPPER_COL are present if self.coverage is not None.

Return type

pandas.DataFrame

predict_uncertainty(df: pandas.core.frame.DataFrame)¶

Makes predictions of prediction intervals for df based on the predictions and self.uncertainty_model.

Parameters: df (pandas.DataFrame) – The dataframe to calculate prediction intervals upon. It should have either self.value_col_ or PREDICT_COL which the prediction interval is based on.
Returns: result_df – The df with prediction interval columns.
Return type: pandas.DataFrame

score(X, y, sample_weight=None)¶

Default scorer for the estimator (Used in GridSearchCV/RandomizedSearchCV if scoring=None)

Notes

If null_model_params is not None, returns R2_null_model_score of model error relative to null model, evaluated by score_func.

If null_model_params is None, returns score_func of the model itself.

By default, grid search (with no scoring parameter) optimizes improvement of score_func against null model.

To optimize a different score function, pass scoring to GridSearchCV/RandomizedSearchCV.

Parameters

X (pandas.DataFrame) – Input timeseries with timestamp column and any additional regressors. Value column, if provided in X, is ignored
y (pandas.Series or numpy.array) – Actual value, used to compute error
sample_weight (pandas.Series or numpy.array) – ignored

Returns

score – Comparison of predictions against null predictions, according to specified score function

Return type

float or None

set_params(**params)¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters: **params (dict) – Estimator parameters.
Returns: self – Estimator instance.
Return type: estimator instance

summary(max_colwidth=20)¶

Creates human readable string of how the model works, including relevant diagnostics These details cannot be extracted from the forecast alone Prints model configuration. Extend this in child class to print the trained model parameters.

Log message is printed to the cst.LOGGER_NAME logger.

class greykite.sklearn.estimator.silverkite_estimator.SilverkiteEstimator(silverkite: greykite.algo.forecast.silverkite.forecast_silverkite.SilverkiteForecast = <greykite.algo.forecast.silverkite.forecast_silverkite.SilverkiteForecast object>, silverkite_diagnostics: greykite.algo.forecast.silverkite.silverkite_diagnostics.SilverkiteDiagnostics = <greykite.algo.forecast.silverkite.silverkite_diagnostics.SilverkiteDiagnostics object>, score_func=<function mean_squared_error>, coverage=None, null_model_params=None, origin_for_time_vars=None, extra_pred_cols=None, drop_pred_cols=None, explicit_pred_cols=None, train_test_thresh=None, training_fraction=None, fit_algorithm_dict=None, daily_event_df_dict=None, fs_components_df= name period order seas_names 0 tod 24.0 3 daily 1 tow 7.0 3 weekly 2 conti_year 1.0 5 yearly, autoreg_dict=None, past_df=None, lagged_regressor_dict=None, changepoints_dict=None, seasonality_changepoints_dict=None, changepoint_detector=None, min_admissible_value=None, max_admissible_value=None, uncertainty_dict=None, normalize_method=None, adjust_anomalous_dict=None, impute_dict=None, regression_weight_col=None, forecast_horizon=None, simulation_based=False, simulation_num=10)[source]¶

Wrapper for forecast.

Parameters

score_func (callable, optional, default mean_squared_error) – See BaseForecastEstimator.
coverage (float between [0.0, 1.0] or None, optional) – See BaseForecastEstimator.
null_model_params (dict or None, optional) – Dictionary with arguments to define DummyRegressor null model, default is None. See BaseForecastEstimator.
fit_algorithm_dict (dict or None, optional) –
How to fit the model. A dictionary with the following optional keys.

"fit_algorithm"str, optional, default “linear”
The type of predictive model used in fitting.

See fit_model_via_design_matrix for available options and their parameters.

"fit_algorithm_params"dict or None, optional, default None
Parameters passed to the requested fit_algorithm. If None, uses the defaults in fit_model_via_design_matrix.
uncertainty_dict (dict or str or None, optional) – How to fit the uncertainty model. See forecast. Note that this is allowed to be “auto”. If None or “auto”, will be set to a default value by coverage before calling forecast_silverkite. See BaseForecastEstimator for details.
fs_components_df (pandas.DataFrame or None, optional) –
A dataframe with information about fourier series generation. If provided, it must contain columns with following names:
- ”name”: name of the timeseries feature (e.g. tod, tow etc.).
- ”period”: Period of the fourier series.
- ”order”: Order of the fourier series. “seas_names”: Label for the type of seasonality (e.g. daily, weekly etc.) and should be unique.
- validate_fs_components_df checks for it, so that component plots don’t have duplicate y-axis labels.
This differs from the expected input of forecast_silverkite where “period”, “order” and “seas_names” are optional. This restriction is to facilitate appropriate computation of component (e.g. trend, seasonalities and holidays) effects. See Notes section in this docstring for a more detailed explanation with examples.

Other parameters are the same as in forecast.

If this Estimator is called from forecast_pipeline, train_test_thresh and training_fraction should almost always be None, because train/test is handled outside this Estimator.

The attributes are the same as BaseSilverkiteEstimator.

See also

None: For details on fit, predict, and component plots.
None: Functions performing the fit and predict.

validate_inputs()[source]¶: Validates the inputs to SilverkiteEstimator.

fit(X, y=None, time_col='ts', value_col='y', **fit_params)[source]¶

Fits Silverkite forecast model.

Parameters

X (pandas.DataFrame) – Input timeseries, with timestamp column, value column, and any additional regressors. The value column is the response, included in X to allow transformation by sklearn.pipeline.
y (ignored) – The original timeseries values, ignored. (The y for fitting is included in X).
time_col (str) – Time column name in X.
value_col (str) – Value column name in X.
fit_params (dict) – additional parameters for null model.

static validate_fs_components_df(fs_components_df)[source]¶

Validates the inputs of a fourier series components dataframe called by SilverkiteEstimator to validate the input fs_components_df.

Parameters

fs_components_df (pandas.DataFrame) –

A DataFrame with information about fourier series generation. Must contain columns with following names:

”name”: name of the timeseries feature (e.g. “tod”, “tow” etc.)
”period”: Period of the fourier series
”order”: Order of the fourier series
”seas_names”: seas_name corresponding to the name (e.g. “daily”, “weekly” etc.).

finish_fit()¶

Makes important values of self.model_dict conveniently accessible.

To be called by subclasses at the end of their fit method. Sets {pred_cols, feature_cols, and coef_}.

fit_uncertainty(df: pandas.core.frame.DataFrame, uncertainty_dict: dict, **kwargs)¶

Fits the uncertainty model with a given df and uncertainty_dict.

Parameters

df (pandas.DataFrame) – A dataframe representing the data to fit the uncertainty model.
uncertainty_dict (dict [str, any]) –
The uncertainty model specification. It should have the following keys:

”uncertainty_method”: a string that is in
UncertaintyMethodEnum.

”params”: a dictionary that includes any additional parameters needed by the uncertainty method.
kwargs (additional parameters to be fed into the uncertainty method.) – These parameters are from the estimator attributes, not given by user.

Returns

Return type

The function sets self.uncertainty_model and does not return anything.

get_max_ar_order()¶

Gets the maximum autoregression order.

Returns: max_ar_order – The maximum autoregression order.
Return type: int

get_params(deep=True)¶

Get parameters for this estimator.

Parameters: deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns: params – Parameter names mapped to their values.
Return type: dict

plot_seasonalities(title=None)¶

Convenience function to plot the data and the seasonality components.

Parameters: title (str, optional, default None) – Plot title.
Returns: fig – Figure.
Return type: plotly.graph_objects.Figure

plot_trend(title=None)¶

Convenience function to plot the data and the trend component.

Parameters: title (str, optional, default None) – Plot title.
Returns: fig – Figure.
Return type: plotly.graph_objects.Figure

plot_trend_changepoint_detection(params=None)¶

Convenience function to plot the original trend changepoint detection results.

Parameters

params (dict or None, default None) –

The parameters in plot. If set to None, all components will be plotted.

Note: seasonality components plotting is not supported currently. plot parameter must be False.

Returns

fig – Figure.

Return type

plotly.graph_objects.Figure

property pred_category¶

A dictionary that stores the predictor names in each category.

This property is not initialized until used. This speeds up the fitting process. The categories includes

“intercept” : the intercept.

“time_features” : the predictors that include TIME_FEATURES but not SEASONALITY_REGEX.

“event_features” : the predictors that include EVENT_PREFIX.

“trend_features” : the predictors that include TREND_REGEX but not SEASONALITY_REGEX.

“seasonality_features” : the predictors that include SEASONALITY_REGEX.

“lag_features” : the predictors that include LAG_REGEX.

“regressor_features” : external regressors and other predictors manually passed to extra_pred_cols, but not in the categories above.

“interaction_features” : the predictors that include interaction terms, i.e., including a colon.

Note that each predictor falls into at least one category. Some “time_features” may also be “trend_features”. Predictors with an interaction are classified into all categories matched by the interaction components. Thus, “interaction_features” are already included in the other categories.

predict(X, y=None)¶

Creates forecast for the dates specified in X.

Parameters

X (pandas.DataFrame) – Input timeseries with timestamp column and any additional regressors. Timestamps are the dates for prediction. Value column, if provided in X, is ignored.
y (ignored.) –

Returns

predictions –

Forecasted values for the dates in X. Columns:

TIME_COL: dates

PREDICTED_COL: predictions

PREDICTED_LOWER_COL: lower bound of predictions, optional

PREDICTED_UPPER_COL: upper bound of predictions, optional

[other columns], optional

PREDICTED_LOWER_COL and PREDICTED_UPPER_COL are present if self.coverage is not None.

Return type

pandas.DataFrame

predict_uncertainty(df: pandas.core.frame.DataFrame)¶

Makes predictions of prediction intervals for df based on the predictions and self.uncertainty_model.

Parameters: df (pandas.DataFrame) – The dataframe to calculate prediction intervals upon. It should have either self.value_col_ or PREDICT_COL which the prediction interval is based on.
Returns: result_df – The df with prediction interval columns.
Return type: pandas.DataFrame

score(X, y, sample_weight=None)¶

Default scorer for the estimator (Used in GridSearchCV/RandomizedSearchCV if scoring=None)

Notes

If null_model_params is not None, returns R2_null_model_score of model error relative to null model, evaluated by score_func.

If null_model_params is None, returns score_func of the model itself.

By default, grid search (with no scoring parameter) optimizes improvement of score_func against null model.

To optimize a different score function, pass scoring to GridSearchCV/RandomizedSearchCV.

Parameters

X (pandas.DataFrame) – Input timeseries with timestamp column and any additional regressors. Value column, if provided in X, is ignored
y (pandas.Series or numpy.array) – Actual value, used to compute error
sample_weight (pandas.Series or numpy.array) – ignored

Returns

score – Comparison of predictions against null predictions, according to specified score function

Return type

float or None

set_params(**params)¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters: **params (dict) – Estimator parameters.
Returns: self – Estimator instance.
Return type: estimator instance

summary(max_colwidth=20)¶

Creates human readable string of how the model works, including relevant diagnostics These details cannot be extracted from the forecast alone Prints model configuration. Extend this in child class to print the trained model parameters.

Log message is printed to the cst.LOGGER_NAME logger.

class greykite.sklearn.estimator.base_silverkite_estimator.BaseSilverkiteEstimator(silverkite: greykite.algo.forecast.silverkite.forecast_silverkite.SilverkiteForecast = <greykite.algo.forecast.silverkite.forecast_silverkite.SilverkiteForecast object>, silverkite_diagnostics: greykite.algo.forecast.silverkite.silverkite_diagnostics.SilverkiteDiagnostics = <greykite.algo.forecast.silverkite.silverkite_diagnostics.SilverkiteDiagnostics object>, score_func: callable = <function mean_squared_error>, coverage: float = None, null_model_params: Optional[Dict] = None, uncertainty_dict: Optional[Dict] = None)[source]¶

A base class for forecast estimators that fit using forecast.

Notes

Allows estimators that fit using forecast to share the same functions for input data validation, fit postprocessing, predict, summary, plot_components, etc.

Subclasses should:

Implement their own __init__ that uses a superset of the parameters here.

Implement their own fit, with this sequence of steps:

calls super().fit

calls SilverkiteForecast.forecast or SimpleSilverkiteForecast.forecast_simple and stores the result in self.model_dict

calls super().finish_fit

Uses coverage to set prediction band width. Even though coverage is not needed by forecast_silverkite, it is included in every BaseForecastEstimator to be used universally for forecast evaluation.

Therefore, uncertainty_dict must be consistent with coverage if provided as a dictionary. If uncertainty_dict is None or “auto”, an appropriate default value is set, according to coverage.

Parameters

score_func (callable, optional, default mean_squared_error) – See BaseForecastEstimator.
coverage (float between [0.0, 1.0] or None, optional) – See BaseForecastEstimator.
null_model_params (dict, optional) – Dictionary with arguments to define DummyRegressor null model, default is None. See BaseForecastEstimator.
uncertainty_dict (dict or str or None, optional) – How to fit the uncertainty model. See forecast. Note that this is allowed to be “auto”. If None or “auto”, will be set to a default value by coverage before calling forecast_silverkite.

silverkite¶

The silverkite algorithm instance used for forecasting

Type: Class or a derived class of SilverkiteForecast

silverkite_diagnostics¶

The silverkite class used for plotting and generating model summary.

Type: Class or a derived class of SilverkiteDiagnostics

model_dict¶

A dict with fitted model and its attributes. The output of forecast.

Type: dict or None

pred_cols¶

Names of the features used in the model.

Type: list [str] or None

feature_cols¶

Column names of the patsy design matrix built by design_mat_from_formula.

Type: list [str] or None

df¶

The training data used to fit the model.

Type: pandas.DataFrame or None

coef_¶

Estimated coefficient matrix for the model. Not available for random forest and gradient boosting methods and set to the default value None.

Type: pandas.DataFrame or None

_pred_category¶

A dictionary with keys being the predictor category and values being the predictors belonging to the category. For details, see pred_category.

Type: dict or None

extra_pred_cols¶

User provided extra predictor names, for details, see SimpleSilverkiteEstimator or SilverkiteEstimator.

Type: list or None

past_df¶

The extra past data before training data used to generate autoregression terms.

Type: pandas.DataFrame or None

forecast¶

Output of predict_silverkite, set by self.predict.

Type: pandas.DataFrame or None

forecast_x_mat¶

The design matrix of the model at the predict time.

Type: pandas.DataFrame or None

model_summary¶

The ModelSummary class.

Type: class or None

See also

None: Function performing the fit and predict.

Notes

The subclasses will pass fs_components_df to forecast_silverkite. The model terms it creates internally are used to generate the component plots.

fourier_series_multi_fcn uses fs_components_df["names"] (e.g. tod, tow) to build the fourier series and to create column names.

fs_components_df["seas_names"] (e.g. daily, weekly) is appended to the column names, if provided.

plot_silverkite_components groups based on fs_components_df["seas_names"] passed to forecast_silverkite during fit. E.g. any column containing daily is added to daily seasonality effect. The reason is as follows:

1. User can provide tow and str_dow for weekly seasonality. These should be aggregated, and we can do that only based on “seas_names”. 2. yearly and quarterly seasonality both use ct1 as “names” column. Only way to distinguish those effects is via “seas_names”. 3. ct1 is also used for growth. If it is interacted with seasonality, the columns become indistinguishable without “seas_names”.

Additionally, the function sets yaxis labels based on seas_names: daily as ylabel is much more informative than tod as ylabel in component plots.

fit(X, y=None, time_col='ts', value_col='y', **fit_params)[source]¶

Pre-processing before fitting Silverkite forecast model.

Parameters

X (pandas.DataFrame) – Input timeseries, with timestamp column, value column, and any additional regressors. The value column is the response, included in X to allow transformation by sklearn.pipeline.
y (ignored) – The original timeseries values, ignored. (The y for fitting is included in X).
time_col (str) – Time column name in X.
value_col (str) – Value column name in X.
fit_params (dict) – additional parameters for null model.

Notes

Subclasses are expected to call this at the beginning of their fit method, before calling forecast.

finish_fit()[source]¶

Makes important values of self.model_dict conveniently accessible.

To be called by subclasses at the end of their fit method. Sets {pred_cols, feature_cols, and coef_}.

predict(X, y=None)[source]¶

Creates forecast for the dates specified in X.

Parameters

X (pandas.DataFrame) – Input timeseries with timestamp column and any additional regressors. Timestamps are the dates for prediction. Value column, if provided in X, is ignored.
y (ignored.) –

Returns

predictions –

Forecasted values for the dates in X. Columns:

TIME_COL: dates

PREDICTED_COL: predictions

PREDICTED_LOWER_COL: lower bound of predictions, optional

PREDICTED_UPPER_COL: upper bound of predictions, optional

[other columns], optional

PREDICTED_LOWER_COL and PREDICTED_UPPER_COL are present if self.coverage is not None.

Return type

pandas.DataFrame

property pred_category¶

A dictionary that stores the predictor names in each category.

This property is not initialized until used. This speeds up the fitting process. The categories includes

“intercept” : the intercept.

“time_features” : the predictors that include TIME_FEATURES but not SEASONALITY_REGEX.

“event_features” : the predictors that include EVENT_PREFIX.

“trend_features” : the predictors that include TREND_REGEX but not SEASONALITY_REGEX.

“seasonality_features” : the predictors that include SEASONALITY_REGEX.

“lag_features” : the predictors that include LAG_REGEX.

“regressor_features” : external regressors and other predictors manually passed to extra_pred_cols, but not in the categories above.

“interaction_features” : the predictors that include interaction terms, i.e., including a colon.

Note that each predictor falls into at least one category. Some “time_features” may also be “trend_features”. Predictors with an interaction are classified into all categories matched by the interaction components. Thus, “interaction_features” are already included in the other categories.

get_max_ar_order()[source]¶

Gets the maximum autoregression order.

Returns: max_ar_order – The maximum autoregression order.
Return type: int

summary(max_colwidth=20)[source]¶

Creates human readable string of how the model works, including relevant diagnostics These details cannot be extracted from the forecast alone Prints model configuration. Extend this in child class to print the trained model parameters.

Log message is printed to the cst.LOGGER_NAME logger.

plot_trend(title=None)[source]¶

Convenience function to plot the data and the trend component.

Parameters: title (str, optional, default None) – Plot title.
Returns: fig – Figure.
Return type: plotly.graph_objects.Figure

plot_seasonalities(title=None)[source]¶

Convenience function to plot the data and the seasonality components.

Parameters: title (str, optional, default None) – Plot title.
Returns: fig – Figure.
Return type: plotly.graph_objects.Figure

plot_trend_changepoint_detection(params=None)[source]¶

Convenience function to plot the original trend changepoint detection results.

Parameters

params (dict or None, default None) –

The parameters in plot. If set to None, all components will be plotted.

Note: seasonality components plotting is not supported currently. plot parameter must be False.

Returns

fig – Figure.

Return type

plotly.graph_objects.Figure

fit_uncertainty(df: pandas.core.frame.DataFrame, uncertainty_dict: dict, **kwargs)¶

Fits the uncertainty model with a given df and uncertainty_dict.

Parameters

df (pandas.DataFrame) – A dataframe representing the data to fit the uncertainty model.
uncertainty_dict (dict [str, any]) –
The uncertainty model specification. It should have the following keys:

”uncertainty_method”: a string that is in
UncertaintyMethodEnum.

”params”: a dictionary that includes any additional parameters needed by the uncertainty method.
kwargs (additional parameters to be fed into the uncertainty method.) – These parameters are from the estimator attributes, not given by user.

Returns

Return type

The function sets self.uncertainty_model and does not return anything.

get_params(deep=True)¶

Get parameters for this estimator.

Parameters: deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns: params – Parameter names mapped to their values.
Return type: dict

predict_uncertainty(df: pandas.core.frame.DataFrame)¶

Makes predictions of prediction intervals for df based on the predictions and self.uncertainty_model.

Parameters: df (pandas.DataFrame) – The dataframe to calculate prediction intervals upon. It should have either self.value_col_ or PREDICT_COL which the prediction interval is based on.
Returns: result_df – The df with prediction interval columns.
Return type: pandas.DataFrame

score(X, y, sample_weight=None)¶

Default scorer for the estimator (Used in GridSearchCV/RandomizedSearchCV if scoring=None)

Notes

If null_model_params is not None, returns R2_null_model_score of model error relative to null model, evaluated by score_func.

If null_model_params is None, returns score_func of the model itself.

By default, grid search (with no scoring parameter) optimizes improvement of score_func against null model.

To optimize a different score function, pass scoring to GridSearchCV/RandomizedSearchCV.

Parameters

X (pandas.DataFrame) – Input timeseries with timestamp column and any additional regressors. Value column, if provided in X, is ignored
y (pandas.Series or numpy.array) – Actual value, used to compute error
sample_weight (pandas.Series or numpy.array) – ignored

Returns

score – Comparison of predictions against null predictions, according to specified score function

Return type

float or None

set_params(**params)¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters: **params (dict) – Estimator parameters.
Returns: self – Estimator instance.
Return type: estimator instance

class greykite.framework.templates.simple_silverkite_template_config.SimpleSilverkiteTemplateOptions(freq: greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_FREQ = <SILVERKITE_FREQ.DAILY: 'DAILY'>, seas: greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_SEAS = <SILVERKITE_SEAS.LT: 'LT'>, gr: greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_GR = <SILVERKITE_GR.LINEAR: 'LINEAR'>, cp: greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_CP = <SILVERKITE_CP.NONE: 'NONE'>, hol: greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_HOL = <SILVERKITE_HOL.NONE: 'NONE'>, feaset: greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_FEASET = <SILVERKITE_FEASET.OFF: 'OFF'>, algo: greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_ALGO = <SILVERKITE_ALGO.LINEAR: 'LINEAR'>, ar: greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_AR = <SILVERKITE_AR.OFF: 'OFF'>, dsi: greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_DSI = <SILVERKITE_DSI.AUTO: 'AUTO'>, wsi: greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_WSI = <SILVERKITE_WSI.AUTO: 'AUTO'>)[source]¶

Defines generic simple silverkite template options.

Attributes can be set to different values using SILVERKITE_COMPONENT_KEYWORDS for high level tuning.

freq represents data frequency.

The other attributes stand for seasonality, growth, changepoints_dict, events, feature_sets_enabled, fit_algorithm and autoregression in ModelComponentsParam, which are used in SimpleSilverkiteTemplate.

freq: greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_FREQ = 'DAILY'¶: Valid values for simple silverkite template string name frequency. See SILVERKITE_FREQ.

seas: greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_SEAS = 'LT'¶: Valid values for simple silverkite template string name seasonality. See SILVERKITE_SEAS.

gr: greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_GR = 'LINEAR'¶: Valid values for simple silverkite template string name growth. See SILVERKITE_GR.

cp: greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_CP = 'NONE'¶: Valid values for simple silverkite template string name changepoints. See SILVERKITE_CP.

hol: greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_HOL = 'NONE'¶: Valid values for simple silverkite template string name holiday. See SILVERKITE_HOL.

feaset: greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_FEASET = 'OFF'¶: Valid values for simple silverkite template string name feature sets enabled. See SILVERKITE_FEASET.

algo: greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_ALGO = 'LINEAR'¶: Valid values for simple silverkite template string name fit algorithm. See SILVERKITE_ALGO.

ar: greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_AR = 'OFF'¶: Valid values for simple silverkite template string name autoregression. See SILVERKITE_AR.

dsi: greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_DSI = 'AUTO'¶: Valid values for simple silverkite template string name max daily seasonality interaction order. See SILVERKITE_DSI.

wsi: greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_WSI = 'AUTO'¶: Valid values for simple silverkite template string name max weekly seasonality interaction order. See SILVERKITE_WSI.

class greykite.framework.templates.silverkite_template.SilverkiteTemplate[source]¶

A template for SilverkiteEstimator.

Takes input data and optional configuration parameters to customize the model. Returns a set of parameters to call forecast_pipeline.

Notes

The attributes of a ForecastConfig for SilverkiteEstimator are:

computation_param: ComputationParam or None, default None
How to compute the result. See ComputationParam.

coverage: float or None, default None
Intended coverage of the prediction bands (0.0 to 1.0). Same as coverage in forecast_pipeline. You may tune how the uncertainty is computed via model_components.uncertainty[“uncertainty_dict”].

evaluation_metric_param: EvaluationMetricParam or None, default None
What metrics to evaluate. See EvaluationMetricParam.

evaluation_period_param: EvaluationPeriodParam or None, default None
How to split data for evaluation. See EvaluationPeriodParam.

forecast_horizon: int or None, default None
Number of periods to forecast into the future. Must be > 0 If None, default is determined from input data frequency Same as forecast_horizon in forecast_pipeline

metadata_param: MetadataParam or None, default None
Information about the input data. See MetadataParam.

model_components_param: ModelComponentsParam or None, default None
Parameters to tune the model. See ModelComponentsParam. The fields are dictionaries with the following items.

See inline comments on which values accept lists for grid search.
seasonality: dict [str, any] or None, optional
How to model the seasonality. A dictionary with keys corresponding to parameters in forecast.

Allowed keys: "fs_components_df".

growth: dict [str, any] or None, optional
How to model the growth.

Allowed keys: None. (Use model_components.custom["extra_pred_cols"] to specify growth terms.)

events: dict [str, any] or None, optional
How to model the holidays/events. A dictionary with keys corresponding to parameters in forecast.

Allowed keys: "daily_event_df_dict".

Note

Event names derived from daily_event_df_dict must be specified via model_components.custom["extra_pred_cols"] to be included in the model. This parameter has no effect on the model unless event names are passed to extra_pred_cols.

The function get_event_pred_cols can be used to extract all event names from daily_event_df_dict.

changepoints: dict [str, any] or None, optional
How to model changes in trend and seasonality. A dictionary with keys corresponding to parameters in forecast.

Allowed keys: “changepoints_dict”, “seasonality_changepoints_dict”, “changepoint_detector”.

autoregression: dict [str, any] or None, optional
Specifies the autoregression configuration. Dictionary with the following optional key:

"autoreg_dict": dict or str or None or a list of such values for grid search
If a dict: A dictionary with arguments for build_autoreg_df. That function’s parameter value_col is inferred from the input of current function self.forecast. Other keys are:

"lag_dict" : dict or None "agg_lag_dict" : dict or None "series_na_fill_func" : callable

If a str: The string will represent a method and a dictionary will be constructed using that str. Currently only implemented method is “auto” which uses __get_default_autoreg_dict to create a dictionary. See more details for above parameters in build_autoreg_df.

regressors: dict [str, any] or None, optional
How to model the regressors.

Allowed keys: None. (Use model_components.custom["extra_pred_cols"] to specify regressors.)

lagged_regressors: dict [str, dict] or None, optional
Specifies the lagged regressors configuration. Dictionary with the following optional key:
"lagged_regressor_dict": dict or None or a list of such values for grid search
A dictionary with arguments for build_autoreg_df_multi. The keys of the dictionary are the target lagged regressor column names. It can leverage the regressors included in df. The value of each key is either a dict or str. If dict, it has the following keys:

"lag_dict" : dict or None "agg_lag_dict" : dict or None "series_na_fill_func" : callable

If str, it represents a method and a dictionary will be constructed using that str. Currently the only implemented method is “auto” which uses SilverkiteForecast’s __get_default_lagged_regressor_dict to create a dictionary for each lagged regressor. An example:
lagged_regressor_dict = {
    "regressor1": {
        "lag_dict": {"orders": [1, 2, 3]},
        "agg_lag_dict": {
            "orders_list": [[7, 7 * 2, 7 * 3]],
            "interval_list": [(8, 7 * 2)]},
        "series_na_fill_func": lambda s: s.bfill().ffill()},
    "regressor2": "auto"}
Check the docstring of build_autoreg_df_multi for more details for each argument.
uncertainty: dict [str, any] or None, optional
How to model the uncertainty. A dictionary with keys corresponding to parameters in forecast.

Allowed keys: "uncertainty_dict".

custom: dict [str, any] or None, optional
Custom parameters that don’t fit the categories above. A dictionary with keys corresponding to parameters in forecast.

Allowed keys:
"silverkite", "silverkite_diagnostics", "origin_for_time_vars", "extra_pred_cols", "drop_pred_cols", "explicit_pred_cols", "fit_algorithm_dict", "min_admissible_value", "max_admissible_value".

Note

"extra_pred_cols" should contain the desired growth terms, regressor names, and event names.

fit_algorithm_dict is a dictionary with fit_algorithm and fit_algorithm_params parameters to forecast:

fit_algorithm_dictdict or None, optional
How to fit the model. A dictionary with the following optional keys.

"fit_algorithm"str, optional, default “linear”
The type of predictive model used in fitting.

See fit_model_via_design_matrix for available options and their parameters.

"fit_algorithm_params"dict or None, optional, default None
Parameters passed to the requested fit_algorithm. If None, uses the defaults in fit_model_via_design_matrix.

hyperparameter_override: dict [str, any] or None or list [dict [str, any] or None], optional
After the above model components are used to create a hyperparameter grid, the result is updated by this dictionary, to create new keys or override existing ones. Allows for complete customization of the grid search.

Keys should have format {named_step}__{parameter_name} for the named steps of the sklearn.pipeline.Pipeline returned by this function. See sklearn.pipeline.Pipeline.

For example:
hyperparameter_override={
    "estimator__origin_for_time_vars": 2018.0,
    "input__response__null__impute_algorithm": "ts_interpolate",
    "input__response__null__impute_params": {"orders": [7, 14]},
    "input__regressors_numeric__normalize__normalize_algorithm": "RobustScaler",
}
If a list of dictionaries, grid search will be done for each dictionary in the list. Each dictionary in the list override the defaults. This enables grid search over specific combinations of parameters to reduce the search space.

For example, the first dictionary could define combinations of parameters for a “complex” model, and the second dictionary could define combinations of parameters for a “simple” model, to prevent mixed combinations of simple and complex.

Or the first dictionary could grid search over fit algorithm, and the second dictionary could use a single fit algorithm and grid search over seasonality.

The result is passed as the param_distributions parameter to sklearn.model_selection.RandomizedSearchCV.
model_template: str
This class only accepts “SK”.

DEFAULT_MODEL_TEMPLATE = 'SK'¶: The default model template. See ModelTemplateEnum. Uses a string to avoid circular imports. Overrides the value from ForecastConfigDefaults.

property allow_model_template_list¶: SilverkiteTemplate does not allow config.model_template to be a list.

property allow_model_components_param_list¶: SilverkiteTemplate does not allow config.model_components_param to be a list.

get_regressor_cols()[source]¶

Returns regressor column names.

Implements the method in BaseTemplate.

The intersection of extra_pred_cols from model components and self.df columns, excluding time_col and value_col.

Returns: regressor_cols – See forecast_pipeline.
Return type: list [str] or None

get_lagged_regressor_info()[source]¶

Returns lagged regressor column names and minimal/maximal lag order. The lag order can be used to check potential imputation in the computation of lags.

Implements the method in BaseTemplate.

Returns

lagged_regressor_info – A dictionary that includes the lagged regressor column names and maximal/minimal lag order The keys are:

lagged_regressor_colslist [str] or None
See forecast_pipeline.

overall_min_lag_order : int or None overall_max_lag_order : int or None

For example:

self.config.model_components_param.lagged_regressors["lagged_regressor_dict"] = [
    {"regressor1": {
        "lag_dict": {"orders": [7]},
        "agg_lag_dict": {
            "orders_list": [[7, 7 * 2, 7 * 3]],
            "interval_list": [(8, 7 * 2)]},
        "series_na_fill_func": lambda s: s.bfill().ffill()}
    },
    {"regressor2": {
        "lag_dict": {"orders": [2]},
        "agg_lag_dict": {
            "orders_list": [[7, 7 * 2]],
            "interval_list": [(8, 7 * 2)]},
        "series_na_fill_func": lambda s: s.bfill().ffill()}
    },
    {"regressor3": "auto"}
]

Then the function returns:

lagged_regressor_info = {
    "lagged_regressor_cols": ["regressor1", "regressor2", "regressor3"],
    "overall_min_lag_order": 2,
    "overall_max_lag_order": 21
}

Note that “regressor3” is skipped as the “auto” option makes sure the lag order is proper.

Return type

dict

get_hyperparameter_grid()[source]¶

Returns hyperparameter grid.

Implements the method in BaseTemplate.

Uses self.time_properties and self.config to generate the hyperparameter grid.

Converts model components and time properties into SilverkiteEstimator hyperparameters.

Notes

forecast_pipeline handles the train/test splits according to EvaluationPeriodParam, so estimator__train_test_thresh and estimator__training_fraction are always None.

estimator__changepoint_detector is always None, to prevent leaking future information into the past. Pass changepoints_dict with method=”auto” for automatic detection.

Returns: hyperparameter_grid – See forecast_pipeline. The output dictionary values are lists, combined in grid search.
Return type: dict, list [dict] or None

static apply_computation_defaults(computation: Optional[greykite.framework.templates.autogen.forecast_config.ComputationParam] = None) → greykite.framework.templates.autogen.forecast_config.ComputationParam ¶

Applies the default ComputationParam values to the given object. If an expected attribute value is provided, the value is unchanged. Otherwise the default value for it is used. Other attributes are untouched. If the input object is None, it creates a ComputationParam object.

Parameters: computation (ComputationParam or None) – The ComputationParam object.
Returns: computation – Valid ComputationParam object with the provided attribute values and the default attribute values if not.
Return type: ComputationParam

static apply_evaluation_metric_defaults(evaluation: Optional[greykite.framework.templates.autogen.forecast_config.EvaluationMetricParam] = None) → greykite.framework.templates.autogen.forecast_config.EvaluationMetricParam ¶

Applies the default EvaluationMetricParam values to the given object. If an expected attribute value is provided, the value is unchanged. Otherwise the default value for it is used. Other attributes are untouched. If the input object is None, it creates a EvaluationMetricParam object.

Parameters: evaluation (EvaluationMetricParam or None) – The EvaluationMetricParam object.
Returns: evaluation – Valid EvaluationMetricParam object with the provided attribute values and the default attribute values if not.
Return type: EvaluationMetricParam

static apply_evaluation_period_defaults(evaluation: Optional[greykite.framework.templates.autogen.forecast_config.EvaluationPeriodParam] = None) → greykite.framework.templates.autogen.forecast_config.EvaluationPeriodParam ¶

Applies the default EvaluationPeriodParam values to the given object. If an expected attribute value is provided, the value is unchanged. Otherwise the default value for it is used. Other attributes are untouched. If the input object is None, it creates a EvaluationPeriodParam object.

Parameters: evaluation (EvaluationPeriodParam or None) – The EvaluationMetricParam object.
Returns: evaluation – Valid EvaluationPeriodParam object with the provided attribute values and the default attribute values if not.
Return type: EvaluationPeriodParam

apply_forecast_config_defaults(config: Optional[greykite.framework.templates.autogen.forecast_config.ForecastConfig] = None) → greykite.framework.templates.autogen.forecast_config.ForecastConfig ¶

Applies the default Forecast Config values to the given config. If an expected attribute value is provided, the value is unchanged. Otherwise the default value for it is used. Other attributes are untouched. If the input config is None, it creates a Forecast Config.

Parameters: config (ForecastConfig or None) – Forecast configuration if available. See ForecastConfig.
Returns: config – A valid Forecast Config which contains the provided attribute values and the default attribute values if not.
Return type: ForecastConfig

static apply_metadata_defaults(metadata: Optional[greykite.framework.templates.autogen.forecast_config.MetadataParam] = None) → greykite.framework.templates.autogen.forecast_config.MetadataParam ¶

Applies the default MetadataParam values to the given object. If an expected attribute value is provided, the value is unchanged. Otherwise the default value for it is used. Other attributes are untouched. If the input object is None, it creates a MetadataParam object.

Parameters: metadata (MetadataParam or None) – The MetadataParam object.
Returns: metadata – Valid MetadataParam object with the provided attribute values and the default attribute values if not.
Return type: MetadataParam

static apply_model_components_defaults(model_components: Optional[Union[greykite.framework.templates.autogen.forecast_config.ModelComponentsParam, List[Optional[greykite.framework.templates.autogen.forecast_config.ModelComponentsParam]]]] = None) → Union[greykite.framework.templates.autogen.forecast_config.ModelComponentsParam, List[greykite.framework.templates.autogen.forecast_config.ModelComponentsParam]]¶

Applies the default ModelComponentsParam values to the given object.

Converts None to a ModelComponentsParam object. Unpacks a list of a single element to the element itself.

Parameters: model_components (ModelComponentsParam or None or list of such items) – The ModelComponentsParam object.
Returns: model_components – Valid ModelComponentsParam object with the provided attribute values and the default attribute values if not.
Return type: ModelComponentsParam or list of such items

apply_model_template_defaults(model_template: Optional[Union[str, List[Optional[str]]]] = None) → Union[str, List[str]]¶

Applies the default ModelComponentsParam values to the given object.

Unpacks a list of a single element to the element itself. Sets default value if None.

Parameters: model_template (str or None or list [None, str]) – The model template name. See valid names in ModelTemplateEnum.
Returns: model_template – The model template name, with defaults value used if not provided.
Return type: str or list [str]

apply_template_for_pipeline_params(df: pandas.core.frame.DataFrame, config: Optional[greykite.framework.templates.autogen.forecast_config.ForecastConfig] = None) → Dict[source]¶

Explicitly calls the method in BaseTemplate to make use of the decorator in this class.

Parameters

df (pandas.DataFrame) – The time series dataframe with time_col and value_col and optional regressor columns.
config (ForecastConfig.) – The ForecastConfig class that includes model training parameters.

Returns

pipeline_parameters – The pipeline parameters consumable by forecast_pipeline.

Return type

dict

property estimator¶: The estimator instance to use as the final step in the pipeline. An instance of greykite.sklearn.estimator.base_forecast_estimator.BaseForecastEstimator.

get_forecast_time_properties()¶

Returns forecast time parameters.

Uses self.df, self.config, self.regressor_cols.

Available parameters:

self.df

self.config

self.score_func

self.score_func_greater_is_better

self.regressor_cols

self.lagged_regressor_cols

self.estimator

self.pipeline

Returns

time_properties – Time properties dictionary (likely produced by get_forecast_time_properties) with keys:

"period"int: Period of each observation (i.e. minimum time between observations, in seconds).
"simple_freq"SimpleTimeFrequencyEnum: SimpleTimeFrequencyEnum member corresponding to data frequency.
"num_training_points"int: Number of observations for training.
"num_training_days"int: Number of days for training.
"start_year"int: Start year of the training period.
"end_year"int: End year of the forecast period.
"origin_for_time_vars"float: Continuous time representation of the first date in df.

Return type

dict [str, any] or None, default None

get_pipeline()¶

Returns pipeline.

Implementation may be overridden by subclass if a different pipeline is desired.

Uses self.estimator, self.score_func, self.score_func_greater_is_better, self.config, self.regressor_cols.

Available parameters:

self.df

self.config

self.score_func

self.score_func_greater_is_better

self.regressor_cols

self.estimator

Returns: pipeline – See forecast_pipeline.
Return type: sklearn.pipeline.Pipeline

static apply_template_decorator(func)[source]¶

Decorator for apply_template_for_pipeline_params function.

Overrides the method in BaseTemplate.

Raises: ValueError if config.model_template != "SK" –

Prophet Template¶

class greykite.framework.templates.prophet_template.ProphetTemplate(estimator: Optional[greykite.sklearn.estimator.base_forecast_estimator.BaseForecastEstimator] = None)[source]¶

A template for ProphetEstimator.

Takes input data and optional configuration parameters to customize the model. Returns a set of parameters to call forecast_pipeline.

Notes

The attributes of a ForecastConfig for ProphetEstimator are:

computation_param: ComputationParam or None, default None
How to compute the result. See ComputationParam.

coverage: float or None, default None
Intended coverage of the prediction bands (0.0 to 1.0) If None, the upper/lower predictions are not returned Same as coverage in forecast_pipeline

evaluation_metric_param: EvaluationMetricParam or None, default None
What metrics to evaluate. See EvaluationMetricParam.

evaluation_period_param: EvaluationPeriodParam or None, default None
How to split data for evaluation. See EvaluationPeriodParam.

forecast_horizon: int or None, default None
Number of periods to forecast into the future. Must be > 0 If None, default is determined from input data frequency Same as forecast_horizon in forecast_pipeline

metadata_param: MetadataParam or None, default None
Information about the input data. See MetadataParam.

model_components_param: ModelComponentsParam or None, default None
Parameters to tune the model. See ModelComponentsParam. The fields are dictionaries with the following items.
seasonality: dict [str, any] or None
Seasonality config dictionary, with the following optional keys.

"seasonality_mode": str or None or list of such values for grid search
Can be ‘additive’ (default) or ‘multiplicative’.

"seasonality_prior_scale": float or None or list of such values for grid search
Parameter modulating the strength of the seasonality model. Larger values allow the model to fit larger seasonal fluctuations, smaller values dampen the seasonality. Specify for individual seasonalities using add_seasonality_dict.

"yearly_seasonality": str or bool or int or list of such values for grid search, default ‘auto’
Determines the yearly seasonality Can be ‘auto’, True, False, or a number of Fourier terms to generate.

"weekly_seasonality": str or bool or int or list of such values for grid search, default ‘auto’
Determines the weekly seasonality Can be ‘auto’, True, False, or a number of Fourier terms to generate.

"daily_seasonality": str or bool or int or list of such values for grid search, default ‘auto’
Determines the daily seasonality Can be ‘auto’, True, False, or a number of Fourier terms to generate.

"add_seasonality_dict": dict or None or list of such values for grid search
dict of custom seasonality parameters to be added to the model, default=None Key is the seasonality component name e.g. ‘monthly’; parameters are specified via dict. See prophet_estimator for details.

growth: dict [str, any] or None
Specifies the growth parameter configuration. Dictionary with the following optional key:

"growth_term": str or None or list of such values for grid search
How to model the growth. Valid options are “linear” and “logistic” Specify a linear or logistic trend, these terms have their origin at the train start date.

events: dict [str, any] or None
Holiday/events configuration dictionary with the following optional keys:

"holiday_lookup_countries": list [str] or “auto” or None
Which countries’ holidays to include. Must contain all the holidays you intend to model. If “auto”, uses default list of countries with large contribution to Internet traffic. If None or an empty list, no holidays are modeled.

"holidays_prior_scale": float or None or list of such values for grid search, default 10.0
Modulates the strength of the holiday effect.

"holiday_pre_num_days": list [int] or None, default 2
Model holiday effects for holiday_pre_num_days days before the holiday. Grid search is not supported. Must be a list with one element or None.

"holiday_post_num_days": list [int] or None, default 2
Model holiday effects for holiday_post_num_days days after the holiday Grid search is not supported. Must be a list with one element or None.

changepoints: dict [str, any] or None
Specifies the changepoint configuration. Dictionary with the following optional keys:

"changepoint_prior_scale"float or None or list of such values for grid search, default 0.05
Parameter modulating the flexibility of the automatic changepoint selection. Large values will allow many changepoints, small values will allow few changepoints.

"changepoints"list [datetime.datetime] or None or list of such values for grid search, default None
List of dates at which to include potential changepoints. If not specified, potential changepoints are selected automatically.

"n_changepoints"int or None or list of such values for grid search, default 25
Number of potential changepoints to include. Not used if input changepoints is supplied. If changepoints is not supplied, then n_changepoints potential changepoints are selected uniformly from the first changepoint_range proportion of the history.

"changepoint_range"float or None or list of such values for grid search, default 0.8
Proportion of history in which trend changepoints will be estimated. Permitted values: (0,1] Not used if input changepoints is supplied.

regressors: dict [str, any] or None
Specifies the regressors to include in the model (e.g. macro-economic factors). Dictionary with the following optional keys:

"add_regressor_dict"dict or None or list of such values for grid search, default None
Dictionary of extra regressors to be modeled. See ProphetEstimator for details.

uncertainty: dict [str, any] or None
Specifies the uncertainty configuration. A dictionary with the following optional keys:

"mcmc_samples"int or None or list of such values for grid search, default 0
if greater than 0, will do full Bayesian inference with the specified number of MCMC samples. If 0, will do MAP estimation.

"uncertainty_samples"int or None or list of such values for grid search, default 1000
Number of simulated draws used to estimate uncertainty intervals. Setting this value to 0 or False will disable uncertainty estimation and speed up the calculation.

hyperparameter_override: dict [str, any] or None or list [dict [str, any] or None]
After the above model components are used to create a hyperparameter grid, the result is updated by this dictionary, to create new keys or override existing ones. Allows for complete customization of the grid search.

Keys should have format {named_step}__{parameter_name} for the named steps of the sklearn.pipeline.Pipeline returned by this function. See sklearn.pipeline.Pipeline.

For example:
hyperparameter_override={
    "estimator__yearly_seasonality": [True, False],
    "estimator__seasonality_prior_scale": [5.0, 15.0],
    "input__response__null__impute_algorithm": "ts_interpolate",
    "input__response__null__impute_params": {"orders": [7, 14]},
    "input__regressors_numeric__normalize__normalize_algorithm": "RobustScaler",
}
If a list of dictionaries, grid search will be done for each dictionary in the list. Each dictionary in the list override the defaults. This enables grid search over specific combinations of parameters to reduce the search space.

For example, the first dictionary could define combinations of parameters for a “complex” model, and the second dictionary could define combinations of parameters for a “simple” model, to prevent mixed combinations of simple and complex.

Or the first dictionary could grid search over fit algorithm, and the second dictionary could use a single fit algorithm and grid search over seasonality.

The result is passed as the param_distributions parameter to sklearn.model_selection.RandomizedSearchCV.
autoregression: dict [str, any] or None
Ignored. Prophet template does not support autoregression.

lagged_regressors: dict [str, any] or None
Ignored. Prophet template does not support lagged regressors.

custom: dict [str, any] or None
Ignored. There are no custom options.
model_template: str
This class only accepts “PROPHET”.

DEFAULT_MODEL_TEMPLATE = 'PROPHET'¶: The default model template. See ModelTemplateEnum. Uses a string to avoid circular imports. Overrides the value from ForecastConfigDefaults.

HOLIDAY_LOOKUP_COUNTRIES_AUTO = ('UnitedStates', 'UnitedKingdom', 'India', 'France', 'China')¶: Default holiday countries to use if countries=’auto’

property allow_model_template_list¶: ProphetTemplate does not allow config.model_template to be a list.

property allow_model_components_param_list¶: ProphetTemplate does not allow config.model_components_param to be a list.

get_prophet_holidays(year_list, countries='auto', lower_window=- 2, upper_window=2)[source]¶

Generates holidays for Prophet model.

Parameters

year_list (list [int]) – List of years for selecting the holidays across given countries.
countries (list [str] or “auto” or None, default “auto”) –
Countries for selecting holidays.
- If “auto”, uses Top Countries for internet traffic.
- If a list, a list of country names.
- If None, the function returns None.
lower_window (int or None, default -2) – Negative integer. Model holiday effects for given number of days before the holiday.
upper_window (int or None, default 2) – Positive integer. Model holiday effects for given number of days after the holiday.

Returns

holidays – holidays dataframe to pass to Prophet’s holidays argument.

Return type

pandas.DataFrame

See also

None, to, None

get_regressor_cols()[source]¶

Returns regressor column names.

Implements the method in BaseTemplate.

Returns: regressor_cols – The names of regressor columns used in any hyperparameter set requested by model_components. None if there are no regressors.
Return type: list [str] or None

apply_prophet_model_components_defaults(model_components=None, time_properties=None)[source]¶

Sets default values for model_components.

Called by get_hyperparameter_grid after time_properties` is defined. Requires ``time_properties as well as model_components so we do not simply override apply_model_components_defaults.

Parameters

model_components (ModelComponentsParam or None, default None) – Configuration of model growth, seasonality, events, etc. See the docstring of this class for details.
time_properties (dict [str, any] or None, default None) –
Time properties dictionary (likely produced by get_forecast_time_properties) with keys:

"period"int
Period of each observation (i.e. minimum time between observations, in seconds).

"simple_freq"SimpleTimeFrequencyEnum
SimpleTimeFrequencyEnum member corresponding to data frequency.

"num_training_points"int
Number of observations for training.

"num_training_days"int
Number of days for training.

"start_year"int
Start year of the training period.

"end_year"int
End year of the forecast period.

"origin_for_time_vars"float
Continuous time representation of the first date in df.

If None, start_year is set to 2015 and end_year to 2030.

Returns

model_components – The provided model_components with default values set

Return type

ModelComponentsParam

get_hyperparameter_grid()[source]¶

Returns hyperparameter grid.

Implements the method in BaseTemplate.

Uses self.time_properties and self.config to generate the hyperparameter grid.

Converts model components and time properties into ProphetEstimator hyperparameters.

Returns

hyperparameter_grid – ProphetEstimator hyperparameters.

See forecast_pipeline. The output dictionary values are lists, combined in grid search.

Return type

dict [str, list [any]] or None

static apply_computation_defaults(computation: Optional[greykite.framework.templates.autogen.forecast_config.ComputationParam] = None) → greykite.framework.templates.autogen.forecast_config.ComputationParam ¶

Applies the default ComputationParam values to the given object. If an expected attribute value is provided, the value is unchanged. Otherwise the default value for it is used. Other attributes are untouched. If the input object is None, it creates a ComputationParam object.

Parameters: computation (ComputationParam or None) – The ComputationParam object.
Returns: computation – Valid ComputationParam object with the provided attribute values and the default attribute values if not.
Return type: ComputationParam

static apply_evaluation_metric_defaults(evaluation: Optional[greykite.framework.templates.autogen.forecast_config.EvaluationMetricParam] = None) → greykite.framework.templates.autogen.forecast_config.EvaluationMetricParam ¶

Applies the default EvaluationMetricParam values to the given object. If an expected attribute value is provided, the value is unchanged. Otherwise the default value for it is used. Other attributes are untouched. If the input object is None, it creates a EvaluationMetricParam object.

Parameters: evaluation (EvaluationMetricParam or None) – The EvaluationMetricParam object.
Returns: evaluation – Valid EvaluationMetricParam object with the provided attribute values and the default attribute values if not.
Return type: EvaluationMetricParam

static apply_evaluation_period_defaults(evaluation: Optional[greykite.framework.templates.autogen.forecast_config.EvaluationPeriodParam] = None) → greykite.framework.templates.autogen.forecast_config.EvaluationPeriodParam ¶

Applies the default EvaluationPeriodParam values to the given object. If an expected attribute value is provided, the value is unchanged. Otherwise the default value for it is used. Other attributes are untouched. If the input object is None, it creates a EvaluationPeriodParam object.

Parameters: evaluation (EvaluationPeriodParam or None) – The EvaluationMetricParam object.
Returns: evaluation – Valid EvaluationPeriodParam object with the provided attribute values and the default attribute values if not.
Return type: EvaluationPeriodParam

apply_forecast_config_defaults(config: Optional[greykite.framework.templates.autogen.forecast_config.ForecastConfig] = None) → greykite.framework.templates.autogen.forecast_config.ForecastConfig ¶

Applies the default Forecast Config values to the given config. If an expected attribute value is provided, the value is unchanged. Otherwise the default value for it is used. Other attributes are untouched. If the input config is None, it creates a Forecast Config.

Parameters: config (ForecastConfig or None) – Forecast configuration if available. See ForecastConfig.
Returns: config – A valid Forecast Config which contains the provided attribute values and the default attribute values if not.
Return type: ForecastConfig

static apply_metadata_defaults(metadata: Optional[greykite.framework.templates.autogen.forecast_config.MetadataParam] = None) → greykite.framework.templates.autogen.forecast_config.MetadataParam ¶

Applies the default MetadataParam values to the given object. If an expected attribute value is provided, the value is unchanged. Otherwise the default value for it is used. Other attributes are untouched. If the input object is None, it creates a MetadataParam object.

Parameters: metadata (MetadataParam or None) – The MetadataParam object.
Returns: metadata – Valid MetadataParam object with the provided attribute values and the default attribute values if not.
Return type: MetadataParam

static apply_model_components_defaults(model_components: Optional[Union[greykite.framework.templates.autogen.forecast_config.ModelComponentsParam, List[Optional[greykite.framework.templates.autogen.forecast_config.ModelComponentsParam]]]] = None) → Union[greykite.framework.templates.autogen.forecast_config.ModelComponentsParam, List[greykite.framework.templates.autogen.forecast_config.ModelComponentsParam]]¶

Applies the default ModelComponentsParam values to the given object.

Converts None to a ModelComponentsParam object. Unpacks a list of a single element to the element itself.

Parameters: model_components (ModelComponentsParam or None or list of such items) – The ModelComponentsParam object.
Returns: model_components – Valid ModelComponentsParam object with the provided attribute values and the default attribute values if not.
Return type: ModelComponentsParam or list of such items

apply_model_template_defaults(model_template: Optional[Union[str, List[Optional[str]]]] = None) → Union[str, List[str]]¶

Applies the default ModelComponentsParam values to the given object.

Unpacks a list of a single element to the element itself. Sets default value if None.

Parameters: model_template (str or None or list [None, str]) – The model template name. See valid names in ModelTemplateEnum.
Returns: model_template – The model template name, with defaults value used if not provided.
Return type: str or list [str]

property estimator¶: The estimator instance to use as the final step in the pipeline. An instance of greykite.sklearn.estimator.base_forecast_estimator.BaseForecastEstimator.

get_forecast_time_properties()¶

Returns forecast time parameters.

Uses self.df, self.config, self.regressor_cols.

Available parameters:

self.df

self.config

self.score_func

self.score_func_greater_is_better

self.regressor_cols

self.lagged_regressor_cols

self.estimator

self.pipeline

Returns

time_properties – Time properties dictionary (likely produced by get_forecast_time_properties) with keys:

"period"int: Period of each observation (i.e. minimum time between observations, in seconds).
"simple_freq"SimpleTimeFrequencyEnum: SimpleTimeFrequencyEnum member corresponding to data frequency.
"num_training_points"int: Number of observations for training.
"num_training_days"int: Number of days for training.
"start_year"int: Start year of the training period.
"end_year"int: End year of the forecast period.
"origin_for_time_vars"float: Continuous time representation of the first date in df.

Return type

dict [str, any] or None, default None

get_lagged_regressor_info()¶

Returns lagged regressor column names and minimal/maximal lag order. The lag order can be used to check potential imputation in the computation of lags.

Can be overridden by subclass.

Returns

lagged_regressor_info – A dictionary that includes the lagged regressor column names and maximal/minimal lag order The keys are:

lagged_regressor_colslist [str] or None
See forecast_pipeline.

overall_min_lag_order : int or None overall_max_lag_order : int or None

Return type

dict

get_pipeline()¶

Returns pipeline.

Implementation may be overridden by subclass if a different pipeline is desired.

Uses self.estimator, self.score_func, self.score_func_greater_is_better, self.config, self.regressor_cols.

Available parameters:

self.df

self.config

self.score_func

self.score_func_greater_is_better

self.regressor_cols

self.estimator

Returns: pipeline – See forecast_pipeline.
Return type: sklearn.pipeline.Pipeline

apply_template_for_pipeline_params(df: pandas.core.frame.DataFrame, config: Optional[greykite.framework.templates.autogen.forecast_config.ForecastConfig] = None) → Dict[source]¶

Explicitly calls the method in BaseTemplate to make use of the decorator in this class.

Parameters

df (pandas.DataFrame) – The time series dataframe with time_col and value_col and optional regressor columns.
config (ForecastConfig.) – The ForecastConfig class that includes model training parameters.

Returns

pipeline_parameters – The pipeline parameters consumable by forecast_pipeline.

Return type

dict

static apply_template_decorator(func)[source]¶

Decorator for apply_template_for_pipeline_params function.

Overrides the method in BaseTemplate.

Raises: ValueError if config.model_template != "PROPHET" –

class greykite.sklearn.estimator.prophet_estimator.ProphetEstimator(score_func=<function mean_squared_error>, coverage=0.8, null_model_params=None, growth='linear', changepoints=None, n_changepoints=25, changepoint_range=0.8, yearly_seasonality='auto', weekly_seasonality='auto', daily_seasonality='auto', holidays=None, seasonality_mode='additive', seasonality_prior_scale=10.0, holidays_prior_scale=10.0, changepoint_prior_scale=0.05, mcmc_samples=0, uncertainty_samples=1000, add_regressor_dict=None, add_seasonality_dict=None)[source]¶

Wrapper for Facebook Prophet model.

Parameters

score_func (callable) – see BaseForecastEstimator
coverage (float between [0.0, 1.0]) – see BaseForecastEstimator
null_model_params (dict with arguments to define DummyRegressor null model, optional, default=None) – see BaseForecastEstimator

add_regressor_dict (dictionary of extra regressors to be added to the model, optional, default=None) –

These should be available for training and entire prediction interval.

Dictionary format:

add_regressor_dict={  # we can add as many regressors as we want, in the following format
    "reg_col1": {
        "prior_scale": 10,
        "standardize": True,
        "mode": 'additive'
    },
    "reg_col2": {
        "prior_scale": 20,
        "standardize": True,
        "mode": 'multiplicative'
    }
}

add_seasonality_dict (dict of custom seasonality parameters to be added to the model, optional, default=None) –

parameter details: https://github.com/facebook/prophet/blob/master/python/fbprophet/forecaster.py - refer to add_seasonality() function. Key is the seasonality component name e.g. ‘monthly’; parameters are specified via dict.

Dictionary format:

add_seasonality_dict={
    'monthly': {
        'period': 30.5,
        'fourier_order': 5
    },
    'weekly': {
        'period': 7,
        'fourier_order': 20,
        'prior_scale': 0.6,
        'mode': 'additive',
        'condition_name': 'condition_col'  # takes a bool column in df with True/False values. This means that
        # the seasonality will only be applied to dates where the condition_name column is True.
    },
    'yearly': {
        'period': 365.25,
        'fourier_order': 10,
        'prior_scale': 0.2,
        'mode': 'additive'
    }
}

Note: If there is a conflict in built-in and custom seasonality e.g. both have “yearly”, then custom seasonality will be used and Model will throw a warning such as: “INFO:fbprophet:Found custom seasonality named “yearly”, disabling built-in yearly seasonality.”

kwargs (additional parameters) –
Other parameters are the same as Prophet model, with one exception: interval_width is specified by coverage.

See source code __init__ for the parameter names, and refer to Prophet documentation for a description:
- https://facebook.github.io/prophet/docs/quick_start.html
- https://github.com/facebook/prophet/blob/c7a150312ad26db5a8ea81d8cd32dd5fd6ce1eda/python/fbprophet/forecaster.py#L30

model¶

Prophet model object

Type: Prophet object

forecast¶

Output of predict method of Prophet.

Type: pandas.DataFrame

fit(X, y=None, time_col='ts', value_col='y', **fit_params)[source]¶

Fits fbprophet model.

Parameters

X (pandas.DataFrame) – Input timeseries, with timestamp column, value column, and any additional regressors. The value column is the response, included in X to allow transformation by sklearn.pipeline.Pipeline
y (ignored) – The original timeseries values, ignored. (The y for fitting is included in X.)
time_col (str) – Time column name in X
value_col (str) – Value column name in X
fit_params (dict) – additional parameters for null model

Returns

self – Fitted model is stored in self.model.

Return type

self

predict(X, y=None)[source]¶

Creates forecast for dates specified in X.

Parameters

X (pandas.DataFrame) – Input timeseries with timestamp column and any additional regressors. Timestamps are the dates for prediction. Value column, if provided in X, is ignored.
y (ignored) –

Returns

predictions –

Forecasted values for the dates in X. Columns:

TIME_COL dates

PREDICTED_COL predictions

PREDICTED_LOWER_COL lower bound of predictions, optional

PREDICTED_UPPER_COL upper bound of predictions, optional

[other columns], optional

PREDICTED_LOWER_COL and PREDICTED_UPPER_COL are present iff coverage is not None

Return type

pandas.DataFrame

summary()[source]¶

Prints input parameters and Prophet model parameters.

Returns: log_message – log message printed to logging.info()
Return type: str

plot_components(uncertainty=True, plot_cap=True, weekly_start=0, yearly_start=0, figsize=None)[source]¶

Plot the Prophet forecast components on the dataset passed to predict.

Will plot whichever are available of: trend, holidays, weekly seasonality, and yearly seasonality.

Parameters

uncertainty (bool, optional, default True) – Boolean to plot uncertainty intervals.
plot_cap (bool, optional, default True) – Boolean indicating if the capacity should be shown in the figure, if available.
weekly_start (int, optional, default 0) – Specifying the start day of the weekly seasonality plot. 0 (default) starts the week on Sunday. 1 shifts by 1 day to Jan 2, and so on.
yearly_start (int, optional, default 0) – Specifying the start day of the yearly seasonality plot. 0 (default) starts the year on Jan 1. 1 shifts by 1 day to Jan 2, and so on.
figsize (tuple , optional, default None) – Width, height in inches.

Returns

fig – A matplotlib figure.

Return type

matplotlib.figure.Figure

fit_uncertainty(df: pandas.core.frame.DataFrame, uncertainty_dict: dict, **kwargs)¶

Fits the uncertainty model with a given df and uncertainty_dict.

Parameters

df (pandas.DataFrame) – A dataframe representing the data to fit the uncertainty model.
uncertainty_dict (dict [str, any]) –
The uncertainty model specification. It should have the following keys:

”uncertainty_method”: a string that is in
UncertaintyMethodEnum.

”params”: a dictionary that includes any additional parameters needed by the uncertainty method.
kwargs (additional parameters to be fed into the uncertainty method.) – These parameters are from the estimator attributes, not given by user.

Returns

Return type

The function sets self.uncertainty_model and does not return anything.

get_params(deep=True)¶

Get parameters for this estimator.

Parameters: deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns: params – Parameter names mapped to their values.
Return type: dict

predict_uncertainty(df: pandas.core.frame.DataFrame)¶

Makes predictions of prediction intervals for df based on the predictions and self.uncertainty_model.

Parameters: df (pandas.DataFrame) – The dataframe to calculate prediction intervals upon. It should have either self.value_col_ or PREDICT_COL which the prediction interval is based on.
Returns: result_df – The df with prediction interval columns.
Return type: pandas.DataFrame

score(X, y, sample_weight=None)¶

Default scorer for the estimator (Used in GridSearchCV/RandomizedSearchCV if scoring=None)

Notes

If null_model_params is not None, returns R2_null_model_score of model error relative to null model, evaluated by score_func.

If null_model_params is None, returns score_func of the model itself.

By default, grid search (with no scoring parameter) optimizes improvement of score_func against null model.

To optimize a different score function, pass scoring to GridSearchCV/RandomizedSearchCV.

Parameters

X (pandas.DataFrame) – Input timeseries with timestamp column and any additional regressors. Value column, if provided in X, is ignored
y (pandas.Series or numpy.array) – Actual value, used to compute error
sample_weight (pandas.Series or numpy.array) – ignored

Returns

score – Comparison of predictions against null predictions, according to specified score function

Return type

float or None

set_params(**params)¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters: **params (dict) – Estimator parameters.
Returns: self – Estimator instance.
Return type: estimator instance

ARIMA Template¶

class greykite.framework.templates.auto_arima_template.AutoArimaTemplate(estimator: greykite.sklearn.estimator.base_forecast_estimator.BaseForecastEstimator = AutoArimaEstimator())[source]¶

A template for AutoArimaEstimator.

Takes input data and optional configuration parameters to customize the model. Returns a set of parameters to call forecast_pipeline.

Notes

The attributes of a ForecastConfig for AutoArimaEstimator are:

computation_param: ComputationParam or None, default None
How to compute the result. See ComputationParam.

coverage: float or None, default None
Intended coverage of the prediction bands (0.0 to 1.0) If None, the upper/lower predictions are not returned Same as coverage in forecast_pipeline

evaluation_metric_param: EvaluationMetricParam or None, default None
What metrics to evaluate. See EvaluationMetricParam.

evaluation_period_param: EvaluationPeriodParam or None, default None
How to split data for evaluation. See EvaluationPeriodParam.

forecast_horizon: int or None, default None
Number of periods to forecast into the future. Must be > 0 If None, default is determined from input data frequency Same as forecast_horizon in forecast_pipeline

metadata_param: MetadataParam or None, default None
Information about the input data. See MetadataParam.

model_components_param: ModelComponentsParam or None, default None
Parameters to tune the model. See ModelComponentsParam. The fields are dictionaries with the following items.
seasonality: dict [str, any] or None
Ignored. Pass the relevant Auto Arima arguments via custom.

growth: dict [str, any] or None
Ignored. Pass the relevant Auto Arima arguments via custom.

events: dict [str, any] or None
Ignored. Pass the relevant Auto Arima arguments via custom.

changepoints: dict [str, any] or None
Ignored. Pass the relevant Auto Arima arguments via custom.

regressors: dict [str, any] or None
Ignored. Auto Arima template currently does not support regressors.

uncertainty: dict [str, any] or None
Ignored. Pass the relevant Auto Arima arguments via custom.

hyperparameter_override: dict [str, any] or None or list [dict [str, any] or None]
After the above model components are used to create a hyperparameter grid, the result is updated by this dictionary, to create new keys or override existing ones. Allows for complete customization of the grid search.

Keys should have format {named_step}__{parameter_name} for the named steps of the sklearn.pipeline.Pipeline returned by this function. See sklearn.pipeline.Pipeline.

For example:
hyperparameter_override={
    "estimator__max_p": [8, 10],
    "estimator__information_criterion": ["bic"],
}
If a list of dictionaries, grid search will be done for each dictionary in the list. Each dictionary in the list override the defaults. This enables grid search over specific combinations of parameters to reduce the search space.

For example, the first dictionary could define combinations of parameters for a “complex” model, and the second dictionary could define combinations of parameters for a “simple” model, to prevent mixed combinations of simple and complex.

Or the first dictionary could grid search over fit algorithm, and the second dictionary could use a single fit algorithm and grid search over seasonality.

The result is passed as the param_distributions parameter to sklearn.model_selection.RandomizedSearchCV.
autoregression: dict [str, any] or None
Ignored. Pass the relevant Auto Arima arguments via custom.

custom: dict [str, any] or None
Any parameter in the AutoArimaEstimator can be passed.
model_template: str
This class only accepts “AUTO_ARIMA”.

property allow_model_template_list¶: AutoArimaTemplate does not allow config.model_template to be a list.

property allow_model_components_param_list¶: AutoArimaTemplate does not allow config.model_components_param to be a list.

get_regressor_cols()[source]¶

Returns regressor column names from the model components.

Currently does not implement regressors.

apply_auto_arima_model_components_defaults(model_components=None)[source]¶

Sets default values for model_components.

Parameters: model_components (ModelComponentsParam or None, default None) – Configuration of model growth, seasonality, events, etc. See the docstring of this class for details.
Returns: model_components – The provided model_components with default values set
Return type: ModelComponentsParam

get_hyperparameter_grid()[source]¶

Returns hyperparameter grid.

Implements the method in BaseTemplate.

Uses self.time_properties and self.config to generate the hyperparameter grid.

Converts model components into AutoArimaEstimator. hyperparameters.

The output dictionary values are lists, combined via grid search in forecast_pipeline.

Parameters

model_components (ModelComponentsParam or None, default None) – Configuration of parameter space to search the order (p, d, q etc.) of SARIMAX model. See auto_arima_template for details.
coverage (float or None, default=0.95) – Intended coverage of the prediction bands (0.0 to 1.0)

Returns

hyperparameter_grid – AutoArimaEstimator hyperparameters.

See forecast_pipeline. The output dictionary values are lists, combined in grid search.

Return type

dict [str, list [any]] or None

apply_template_for_pipeline_params(df: pandas.core.frame.DataFrame, config: Optional[greykite.framework.templates.autogen.forecast_config.ForecastConfig] = None) → Dict[source]¶

Explicitly calls the method in BaseTemplate to make use of the decorator in this class.

Parameters

df (pandas.DataFrame) – The time series dataframe with time_col and value_col and optional regressor columns.
config (ForecastConfig.) – The ForecastConfig class that includes model training parameters.

Returns

pipeline_parameters – The pipeline parameters consumable by forecast_pipeline.

Return type

dict

static apply_computation_defaults(computation: Optional[greykite.framework.templates.autogen.forecast_config.ComputationParam] = None) → greykite.framework.templates.autogen.forecast_config.ComputationParam ¶

Applies the default ComputationParam values to the given object. If an expected attribute value is provided, the value is unchanged. Otherwise the default value for it is used. Other attributes are untouched. If the input object is None, it creates a ComputationParam object.

Parameters: computation (ComputationParam or None) – The ComputationParam object.
Returns: computation – Valid ComputationParam object with the provided attribute values and the default attribute values if not.
Return type: ComputationParam

static apply_evaluation_metric_defaults(evaluation: Optional[greykite.framework.templates.autogen.forecast_config.EvaluationMetricParam] = None) → greykite.framework.templates.autogen.forecast_config.EvaluationMetricParam ¶

Applies the default EvaluationMetricParam values to the given object. If an expected attribute value is provided, the value is unchanged. Otherwise the default value for it is used. Other attributes are untouched. If the input object is None, it creates a EvaluationMetricParam object.

Parameters: evaluation (EvaluationMetricParam or None) – The EvaluationMetricParam object.
Returns: evaluation – Valid EvaluationMetricParam object with the provided attribute values and the default attribute values if not.
Return type: EvaluationMetricParam

static apply_evaluation_period_defaults(evaluation: Optional[greykite.framework.templates.autogen.forecast_config.EvaluationPeriodParam] = None) → greykite.framework.templates.autogen.forecast_config.EvaluationPeriodParam ¶

Applies the default EvaluationPeriodParam values to the given object. If an expected attribute value is provided, the value is unchanged. Otherwise the default value for it is used. Other attributes are untouched. If the input object is None, it creates a EvaluationPeriodParam object.

Parameters: evaluation (EvaluationPeriodParam or None) – The EvaluationMetricParam object.
Returns: evaluation – Valid EvaluationPeriodParam object with the provided attribute values and the default attribute values if not.
Return type: EvaluationPeriodParam

apply_forecast_config_defaults(config: Optional[greykite.framework.templates.autogen.forecast_config.ForecastConfig] = None) → greykite.framework.templates.autogen.forecast_config.ForecastConfig ¶

Applies the default Forecast Config values to the given config. If an expected attribute value is provided, the value is unchanged. Otherwise the default value for it is used. Other attributes are untouched. If the input config is None, it creates a Forecast Config.

Parameters: config (ForecastConfig or None) – Forecast configuration if available. See ForecastConfig.
Returns: config – A valid Forecast Config which contains the provided attribute values and the default attribute values if not.
Return type: ForecastConfig

static apply_metadata_defaults(metadata: Optional[greykite.framework.templates.autogen.forecast_config.MetadataParam] = None) → greykite.framework.templates.autogen.forecast_config.MetadataParam ¶

Applies the default MetadataParam values to the given object. If an expected attribute value is provided, the value is unchanged. Otherwise the default value for it is used. Other attributes are untouched. If the input object is None, it creates a MetadataParam object.

Parameters: metadata (MetadataParam or None) – The MetadataParam object.
Returns: metadata – Valid MetadataParam object with the provided attribute values and the default attribute values if not.
Return type: MetadataParam

static apply_model_components_defaults(model_components: Optional[Union[greykite.framework.templates.autogen.forecast_config.ModelComponentsParam, List[Optional[greykite.framework.templates.autogen.forecast_config.ModelComponentsParam]]]] = None) → Union[greykite.framework.templates.autogen.forecast_config.ModelComponentsParam, List[greykite.framework.templates.autogen.forecast_config.ModelComponentsParam]]¶

Applies the default ModelComponentsParam values to the given object.

Converts None to a ModelComponentsParam object. Unpacks a list of a single element to the element itself.

Parameters: model_components (ModelComponentsParam or None or list of such items) – The ModelComponentsParam object.
Returns: model_components – Valid ModelComponentsParam object with the provided attribute values and the default attribute values if not.
Return type: ModelComponentsParam or list of such items

apply_model_template_defaults(model_template: Optional[Union[str, List[Optional[str]]]] = None) → Union[str, List[str]]¶

Applies the default ModelComponentsParam values to the given object.

Unpacks a list of a single element to the element itself. Sets default value if None.

Parameters: model_template (str or None or list [None, str]) – The model template name. See valid names in ModelTemplateEnum.
Returns: model_template – The model template name, with defaults value used if not provided.
Return type: str or list [str]

static apply_template_decorator(func)[source]¶

Decorator for apply_template_for_pipeline_params function.

Overrides the method in BaseTemplate.

Raises: ValueError if config.model_template != "AUTO_ARIMA" –

property estimator¶: The estimator instance to use as the final step in the pipeline. An instance of greykite.sklearn.estimator.base_forecast_estimator.BaseForecastEstimator.

get_forecast_time_properties()¶

Returns forecast time parameters.

Uses self.df, self.config, self.regressor_cols.

Available parameters:

self.df

self.config

self.score_func

self.score_func_greater_is_better

self.regressor_cols

self.lagged_regressor_cols

self.estimator

self.pipeline

Returns

time_properties – Time properties dictionary (likely produced by get_forecast_time_properties) with keys:

"period"int: Period of each observation (i.e. minimum time between observations, in seconds).
"simple_freq"SimpleTimeFrequencyEnum: SimpleTimeFrequencyEnum member corresponding to data frequency.
"num_training_points"int: Number of observations for training.
"num_training_days"int: Number of days for training.
"start_year"int: Start year of the training period.
"end_year"int: End year of the forecast period.
"origin_for_time_vars"float: Continuous time representation of the first date in df.

Return type

dict [str, any] or None, default None

get_lagged_regressor_info()¶

Returns lagged regressor column names and minimal/maximal lag order. The lag order can be used to check potential imputation in the computation of lags.

Can be overridden by subclass.

Returns

lagged_regressor_info – A dictionary that includes the lagged regressor column names and maximal/minimal lag order The keys are:

lagged_regressor_colslist [str] or None
See forecast_pipeline.

overall_min_lag_order : int or None overall_max_lag_order : int or None

Return type

dict

get_pipeline()¶

Returns pipeline.

Implementation may be overridden by subclass if a different pipeline is desired.

Uses self.estimator, self.score_func, self.score_func_greater_is_better, self.config, self.regressor_cols.

Available parameters:

self.df

self.config

self.score_func

self.score_func_greater_is_better

self.regressor_cols

self.estimator

Returns: pipeline – See forecast_pipeline.
Return type: sklearn.pipeline.Pipeline

class greykite.sklearn.estimator.auto_arima_estimator.AutoArimaEstimator(score_func: callable = <function mean_squared_error>, coverage: float = 0.9, null_model_params: Optional[Dict] = None, regressor_cols: Optional[List[str]] = None, freq: Optional[float] = None, start_p: Optional[int] = 2, d: Optional[int] = None, start_q: Optional[int] = 2, max_p: Optional[int] = 5, max_d: Optional[int] = 2, max_q: Optional[int] = 5, start_P: Optional[int] = 1, D: Optional[int] = None, start_Q: Optional[int] = 1, max_P: Optional[int] = 2, max_D: Optional[int] = 1, max_Q: Optional[int] = 2, max_order: Optional[int] = 5, m: Optional[int] = 1, seasonal: Optional[bool] = True, stationary: Optional[bool] = False, information_criterion: Optional[str] = 'aic', alpha: Optional[int] = 0.05, test: Optional[str] = 'kpss', seasonal_test: Optional[str] = 'ocsb', stepwise: Optional[bool] = True, n_jobs: Optional[int] = 1, start_params: Optional[Dict] = None, trend: Optional[str] = None, method: Optional[str] = 'lbfgs', maxiter: Optional[int] = 50, offset_test_args: Optional[Dict] = None, seasonal_test_args: Optional[Dict] = None, suppress_warnings: Optional[bool] = True, error_action: Optional[str] = 'trace', trace: Optional[Union[int, bool]] = False, random: Optional[bool] = False, random_state: Optional[Union[int, callable]] = None, n_fits: Optional[int] = 10, out_of_sample_size: Optional[int] = 0, scoring: Optional[str] = 'mse', scoring_args: Optional[Dict] = None, with_intercept: Optional[Union[bool, str]] = 'auto', return_conf_int: Optional[bool] = True, dynamic: Optional[bool] = False)[source]¶

Wrapper for pmdarima.arima.AutoARIMA. It currently does not handle the regressor issue when there is gap between train and predict periods.

Parameters

score_func (callable) – see BaseForecastEstimator.
coverage (float between [0.0, 1.0]) – see BaseForecastEstimator.
null_model_params (dict with arguments to define DummyRegressor null model, optional, default=None) – see BaseForecastEstimator.
regressor_cols (list [str], optional, default None) – A list of regressor columns used during training and prediction. If None, no regressor columns are used.
AutoArima documentation for rest of the parameter descriptions (See) –
- https://alkaline-ml.com/pmdarima/modules/generated/pmdarima.arima.AutoARIMA.html#pmdarima.arima.AutoARIMA

model¶

Auto arima model object

Type: AutoArima object

fit_df¶

The training data used to fit the model.

Type: pandas.DataFrame or None

forecast¶

Output of the predict method of AutoArima.

Type: pandas.DataFrame

fit(X, y=None, time_col='ts', value_col='y', **fit_params)[source]¶

Fits ARIMA forecast model.

Parameters

X (pandas.DataFrame) – Input timeseries, with timestamp column, value column, and any additional regressors. The value column is the response, included in X to allow transformation by sklearn.pipeline.Pipeline
y (ignored) – The original timeseries values, ignored. (The y for fitting is included in X.)
time_col (str) – Time column name in X
value_col (str) – Value column name in X
fit_params (dict) – additional parameters for null model

Returns

self – Fitted model is stored in self.model.

Return type

self

predict(X, y=None)[source]¶

Creates forecast for the dates specified in X. Currently does not support the regressor case where there is gap between train and predict periods.

Parameters

X (pandas.DataFrame) – Input timeseries with timestamp column and any additional regressors. Timestamps are the dates for prediction. Value column, if provided in X, is ignored.
y (ignored.) –

Returns

predictions –

Forecasted values for the dates in X. Columns:

TIME_COL: dates

PREDICTED_COL: predictions

PREDICTED_LOWER_COL: lower bound of predictions

PREDICTED_UPPER_COL: upper bound of predictions

Return type

pandas.DataFrame

summary()[source]¶

Creates human readable string of how the model works, including relevant diagnostics These details cannot be extracted from the forecast alone Prints model configuration. Extend this in child class to print the trained model parameters.

Log message is printed to the cst.LOGGER_NAME logger.

fit_uncertainty(df: pandas.core.frame.DataFrame, uncertainty_dict: dict, **kwargs)¶

Fits the uncertainty model with a given df and uncertainty_dict.

Parameters

df (pandas.DataFrame) – A dataframe representing the data to fit the uncertainty model.
uncertainty_dict (dict [str, any]) –
The uncertainty model specification. It should have the following keys:

”uncertainty_method”: a string that is in
UncertaintyMethodEnum.

”params”: a dictionary that includes any additional parameters needed by the uncertainty method.
kwargs (additional parameters to be fed into the uncertainty method.) – These parameters are from the estimator attributes, not given by user.

Returns

Return type

The function sets self.uncertainty_model and does not return anything.

get_params(deep=True)¶

Get parameters for this estimator.

Parameters: deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns: params – Parameter names mapped to their values.
Return type: dict

predict_uncertainty(df: pandas.core.frame.DataFrame)¶

Makes predictions of prediction intervals for df based on the predictions and self.uncertainty_model.

Parameters: df (pandas.DataFrame) – The dataframe to calculate prediction intervals upon. It should have either self.value_col_ or PREDICT_COL which the prediction interval is based on.
Returns: result_df – The df with prediction interval columns.
Return type: pandas.DataFrame

score(X, y, sample_weight=None)¶

Default scorer for the estimator (Used in GridSearchCV/RandomizedSearchCV if scoring=None)

Notes

If null_model_params is not None, returns R2_null_model_score of model error relative to null model, evaluated by score_func.

If null_model_params is None, returns score_func of the model itself.

By default, grid search (with no scoring parameter) optimizes improvement of score_func against null model.

To optimize a different score function, pass scoring to GridSearchCV/RandomizedSearchCV.

Parameters

X (pandas.DataFrame) – Input timeseries with timestamp column and any additional regressors. Value column, if provided in X, is ignored
y (pandas.Series or numpy.array) – Actual value, used to compute error
sample_weight (pandas.Series or numpy.array) – ignored

Returns

score – Comparison of predictions against null predictions, according to specified score function

Return type

float or None

set_params(**params)¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters: **params (dict) – Estimator parameters.
Returns: self – Estimator instance.
Return type: estimator instance

Forecast Pipeline¶

greykite.framework.pipeline.pipeline.forecast_pipeline(df: pandas.core.frame.DataFrame, time_col='ts', value_col='y', date_format=None, tz=None, freq=None, train_end_date=None, anomaly_info=None, pipeline=None, regressor_cols=None, lagged_regressor_cols=None, estimator=SimpleSilverkiteEstimator(), hyperparameter_grid=None, hyperparameter_budget=None, n_jobs=1, verbose=1, forecast_horizon=None, coverage=0.95, test_horizon=None, periods_between_train_test=None, agg_periods=None, agg_func=None, score_func='MeanAbsolutePercentError', score_func_greater_is_better=False, cv_report_metrics='ALL', null_model_params=None, relative_error_tolerance=None, cv_horizon=None, cv_min_train_periods=None, cv_expanding_window=False, cv_use_most_recent_splits=False, cv_periods_between_splits=None, cv_periods_between_train_test=None, cv_max_splits=3)[source]¶

Computation pipeline for end-to-end forecasting.

Trains a forecast model end-to-end:

checks input data

runs cross-validation to select optimal hyperparameters e.g. best model

evaluates best model on test set

provides forecast of best model (re-trained on all data) into the future

Returns forecasts with methods to plot and see diagnostics. Also returns the fitted pipeline and CV results.

Provides a high degree of customization over training and evaluation parameters:

model

cross validation

evaluation

forecast horizon

See test cases for examples.

Parameters

df (pandas.DataFrame) – Timeseries data to forecast. Contains columns [time_col, value_col], and optional regressor columns Regressor columns should include future values for prediction
time_col (str, default TIME_COL in constants.py) – name of timestamp column in df
value_col (str, default VALUE_COL in constants.py) – name of value column in df (the values to forecast)
date_format (str or None, default None) – strftime format to parse time column, eg %m/%d/%Y. Note that %f will parse all the way up to nanoseconds. If None (recommended), inferred by pandas.to_datetime.
tz (str or None, default None) – Passed to pandas.tz_localize to localize the timestamp
freq (str or None, default None) – Frequency of input data. Used to generate future dates for prediction. Frequency strings can have multiples, e.g. ‘5H’. See https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases for a list of frequency aliases. If None, inferred by pandas.infer_freq. Provide this parameter if df has missing timepoints.
train_end_date (datetime.datetime, optional, default None) – Last date to use for fitting the model. Forecasts are generated after this date. If None, it is set to the last date with a non-null value in value_col of df.
anomaly_info (dict or list [dict] or None, default None) –
Anomaly adjustment info. Anomalies in df are corrected before any forecasting is done.

If None, no adjustments are made.

A dictionary containing the parameters to adjust_anomalous_data. See that function for details. The possible keys are:

"value_col"str
The name of the column in df to adjust. You may adjust the value to forecast as well as any numeric regressors.

"anomaly_df"pandas.DataFrame
Adjustments to correct the anomalies.

"start_date_col": str, default START_DATE_COL
Start date column in anomaly_df.

"end_date_col": str, default END_DATE_COL
End date column in anomaly_df.

"adjustment_delta_col": str or None, default None
Impact column in anomaly_df.

"filter_by_dict": dict or None, default None
Used to filter anomaly_df to the relevant anomalies for the value_col in this dictionary. Key specifies the column name, value specifies the filter value.

"filter_by_value_col"": str or None, default None
Adds {filter_by_value_col: value_col} to filter_by_dict if not None, for the value_col in this dictionary.

"adjustment_method"str (“add” or “subtract”), default “add”
How to make the adjustment, if adjustment_delta_col is provided.

Accepts a list of such dictionaries to adjust multiple columns in df.
pipeline (sklearn.pipeline.Pipeline or None, default None) – Pipeline to fit. The final named step must be called “estimator”. If None, will use the default Pipeline from get_basic_pipeline.
regressor_cols (list [str] or None, default None) – A list of regressor columns used in the training and prediction DataFrames. It should contain only the regressors that are being used in the grid search. If None, no regressor columns are used. Regressor columns that are unavailable in df are dropped.
lagged_regressor_cols (list [str] or None, default None) – A list of additional columns needed for lagged regressors in the training and prediction DataFrames. This list can have overlap with regressor_cols. If None, no additional columns are added to the DataFrame. Lagged regressor columns that are unavailable in df are dropped.
estimator (instance of an estimator that implements greykite.algo.models.base_forecast_estimator.BaseForecastEstimator) – Estimator to use as the final step in the pipeline. Ignored if pipeline is provided.
forecast_horizon (int or None, default None) – Number of periods to forecast into the future. Must be > 0. If None, default is determined from input data frequency
coverage (float or None, default=0.95) – Intended coverage of the prediction bands (0.0 to 1.0) If None, the upper/lower predictions are not returned Ignored if pipeline is provided. Uses coverage of the pipeline estimator instead.
test_horizon (int or None, default None) – Numbers of periods held back from end of df for test. The rest is used for cross validation. If None, default is forecast_horizon. Set to 0 to skip backtest.
periods_between_train_test (int or None, default None) – Number of periods for the gap between train and test data. If None, default is 0.
agg_periods (int or None, default None) –
Number of periods to aggregate before evaluation.

Model is fit and forecasted on the dataset’s original frequency.

Before evaluation, the actual and forecasted values are aggregated, using rolling windows of size agg_periods and the function agg_func. (e.g. if the dataset is hourly, use agg_periods=24, agg_func=np.sum, to evaluate performance on the daily totals).

If None, does not aggregate before evaluation.

Currently, this is only used when calculating CV metrics and the R2_null_model_score metric in backtest/forecast. No pre-aggregation is applied for the other backtest/forecast evaluation metrics.
agg_func (callable or None, default None) –
Takes an array and returns a number, e.g. np.max, np.sum.

Defines how to aggregate rolling windows of actual and predicted values before evaluation.

Ignored if agg_periods is None.

Currently, this is only used when calculating CV metrics and the R2_null_model_score metric in backtest/forecast. No pre-aggregation is applied for the other backtest/forecast evaluation metrics.
score_func (str or callable, default EvaluationMetricEnum.MeanAbsolutePercentError.name) – Score function used to select optimal model in CV. If a callable, takes arrays y_true, y_pred and returns a float. If a string, must be either a EvaluationMetricEnum member name or FRACTION_OUTSIDE_TOLERANCE.
score_func_greater_is_better (bool, default False) – True if score_func is a score function, meaning higher is better, and False if it is a loss function, meaning lower is better. Must be provided if score_func is a callable (custom function). Ignored if score_func is a string, because the direction is known.
cv_report_metrics (str, or list [str], or None, default CV_REPORT_METRICS_ALL) –
Additional metrics to compute during CV, besides the one specified by score_func.
- If the string constant greykite.framework.constants.CV_REPORT_METRICS_ALL, computes all metrics in EvaluationMetricEnum. Also computes FRACTION_OUTSIDE_TOLERANCE if relative_error_tolerance is not None. The results are reported by the short name (.get_metric_name()) for EvaluationMetricEnum members and FRACTION_OUTSIDE_TOLERANCE_NAME for FRACTION_OUTSIDE_TOLERANCE. These names appear in the keys of forecast_result.grid_search.cv_results_ returned by this function.
- If a list of strings, each of the listed metrics is computed. Valid strings are EvaluationMetricEnum member names and FRACTION_OUTSIDE_TOLERANCE.
  
  For example:
  ["MeanSquaredError", "MeanAbsoluteError", "MeanAbsolutePercentError", "MedianAbsolutePercentError", "FractionOutsideTolerance2"]
- If None, no additional metrics are computed.
null_model_params (dict or None, default None) –
Defines baseline model to compute R2_null_model_score evaluation metric. R2_null_model_score is the improvement in the loss function relative to a null model. It can be used to evaluate model quality with respect to a simple baseline. For details, see r2_null_model_score.

The null model is a DummyRegressor, which returns constant predictions.

Valid keys are “strategy”, “constant”, “quantile”. See DummyRegressor. For example:
```
null_model_params = {
    "strategy": "mean",
}
null_model_params = {
    "strategy": "median",
}
null_model_params = {
    "strategy": "quantile",
    "quantile": 0.8,
}
null_model_params = {
    "strategy": "constant",
    "constant": 2.0,
}
```
If None, R2_null_model_score is not calculated.

Note: CV model selection always optimizes score_func`, not the ``R2_null_model_score.
relative_error_tolerance (float or None, default None) – Threshold to compute the Outside Tolerance metric, defined as the fraction of forecasted values whose relative error is strictly greater than relative_error_tolerance. For example, 0.05 allows for 5% relative error. If None, the metric is not computed.
hyperparameter_grid (dict, list [dict] or None, default None) –
Sets properties of the steps in the pipeline, and specifies combinations to search over. Should be valid input to sklearn.model_selection.GridSearchCV (param_grid) or sklearn.model_selection.RandomizedSearchCV (param_distributions).

Prefix transform/estimator attributes by the name of the step in the pipeline. See details at: https://scikit-learn.org/stable/modules/compose.html#nested-parameters

If None, uses the default pipeline parameters.
hyperparameter_budget (int or None, default None) –
Max number of hyperparameter sets to try within the hyperparameter_grid search space

Runs a full grid search if hyperparameter_budget is sufficient to exhaust full hyperparameter_grid, otherwise samples uniformly at random from the space.

If None, uses defaults:
- full grid search if all values are constant
- 10 if any value is a distribution to sample from
n_jobs (int or None, default COMPUTATION_N_JOBS) – Number of jobs to run in parallel (the maximum number of concurrently running workers). -1 uses all CPUs. -2 uses all CPUs but one. None is treated as 1 unless in a joblib.Parallel backend context that specifies otherwise.
verbose (int, default 1) – Verbosity level during CV. if > 0, prints number of fits if > 1, prints fit parameters, total score + fit time if > 2, prints train/test scores
cv_horizon (int or None, default None) – Number of periods in each CV test set If None, default is forecast_horizon. Set either cv_horizon or cv_max_splits to 0 to skip CV.
cv_min_train_periods (int or None, default None) – Minimum number of periods for training each CV fold. If cv_expanding_window is False, every training period is this size If None, default is 2 * cv_horizon
cv_expanding_window (bool, default False) – If True, training window for each CV split is fixed to the first available date. Otherwise, train start date is sliding, determined by cv_min_train_periods.
cv_use_most_recent_splits (bool, default False) – If True, splits from the end of the dataset are used. Else a sampling strategy is applied. Check _sample_splits for details.
cv_periods_between_splits (int or None, default None) – Number of periods to slide the test window between CV splits If None, default is cv_horizon
cv_periods_between_train_test (int or None, default None) – Number of periods for the gap between train and test in a CV split. If None, default is periods_between_train_test.
cv_max_splits (int or None, default 3) – Maximum number of CV splits. Given the above configuration, samples up to max_splits train/test splits, preferring splits toward the end of available data. If None, uses all splits. Set either cv_horizon or cv_max_splits to 0 to skip CV.

Returns

forecast_result – Forecast result. See ForecastResult for details.

If cv_horizon=0, forecast_result.grid_search.best_estimator_ and forecast_result.grid_search.best_params_ attributes are defined according to the provided single set of parameters. There must be a single set of parameters to skip cross-validation.

If test_horizon=0, forecast_result.backtest is None.

Return type

ForecastResult

class greykite.framework.pipeline.pipeline.ForecastResult(timeseries: greykite.framework.input.univariate_time_series.UnivariateTimeSeries = None, grid_search: sklearn.model_selection._search.RandomizedSearchCV = None, model: sklearn.pipeline.Pipeline = None, backtest: greykite.framework.output.univariate_forecast.UnivariateForecast = None, forecast: greykite.framework.output.univariate_forecast.UnivariateForecast = None)[source]¶

Forecast results. Contains results from cross-validation, backtest, and forecast, the trained model, and the original input data.

timeseries: greykite.framework.input.univariate_time_series.UnivariateTimeSeries = None¶: Input time series in standard format with stats and convenient plot functions.

grid_search: sklearn.model_selection._search.RandomizedSearchCV = None¶

Result of cross-validation grid search on training dataset. The relevant attributes are:

cv_results_ cross-validation scores

best_estimator_ the model used for backtesting

best_params_ the optimal parameters used for backtesting.

Also see summarize_grid_search_results. We recommend using this function to extract results, rather than accessing cv_results_ directly.

model: sklearn.pipeline.Pipeline = None¶: Model fitted on full dataset, using the best parameters selected via cross-validation. Has .fit(), .predict(), and diagnostic functions depending on the model.

backtest: greykite.framework.output.univariate_forecast.UnivariateForecast = None¶: Forecast on backtest period. Backtest period is a holdout test set to check forecast quality against the most recent actual values available. The best model from cross validation is refit on data prior to this period. The timestamps in backtest.df are sorted in ascending order. Has a .plot() method and attributes to get forecast vs actuals, evaluation results.

forecast: greykite.framework.output.univariate_forecast.UnivariateForecast = None¶: Forecast on future period. Future dates are after the train end date, following the holdout test set. The best model from cross validation is refit on data prior to this period. The timestamps in forecast.df are sorted in ascending order. Has a .plot() method and attributes to get forecast vs actuals, evaluation results.

Template Output¶

class greykite.framework.input.univariate_time_series.UnivariateTimeSeries[source]¶

Defines univariate time series input. The dataset can include regressors, but only one metric is designated as the target metric to forecast.

Loads time series into a standard format. Provides statistics, plotting functions, and ability to generate future dataframe for prediction.

df¶

Data frame containing timestamp and value, with standardized column names for internal use (TIME_COL, VALUE_COL). Rows are sorted by time index, and missing gaps between dates are filled in so that dates are spaced at regular intervals. Values are adjusted for anomalies according to anomaly_info. The index can be timezone aware (but TIME_COL is not).

Type: pandas.DataFrame

y¶

Value of time series to forecast.

Type: pandas.Series, dtype float64

time_stats¶

Summary statistics about the timestamp column.

Type: dict

value_stats¶

Summary statistics about the value column.

Type: dict

original_time_col¶

Name of time column in original input data.

Type: str

original_value_col¶

Name of value column in original input data.

Type: str

regressor_cols¶

A list of regressor columns in the training and prediction DataFrames.

Type: list [str]

lagged_regressor_cols¶

A list of additional columns needed for lagged regressors in the training and prediction DataFrames.

Type: list [str]

last_date_for_val¶

Date or timestamp corresponding to last non-null value in df[original_value_col].

Type: datetime.datetime or None, default None

last_date_for_reg¶

Date or timestamp corresponding to last non-null value in df[regressor_cols]. If regressor_cols is None, last_date_for_reg is None.

Type: datetime.datetime or None, default None

last_date_for_lag_reg¶

Date or timestamp corresponding to last non-null value in df[lagged_regressor_cols]. If lagged_regressor_cols is None, last_date_for_lag_reg is None.

Type: datetime.datetime or None, default None

train_end_date¶

Last date or timestamp in fit_df. It is always less than or equal to minimum non-null values of last_date_for_val and last_date_for_reg.

Type: datetime.datetime

fit_cols¶

A list of columns used in the training and prediction DataFrames.

Type: list [str]

fit_df¶

Data frame containing timestamp and value, with standardized column names for internal use. Will be used for fitting (train, cv, backtest).

Type: pandas.DataFrame

fit_y¶

Value of time series for fit_df.

Type: pandas.Series, dtype float64

freq¶

timeseries frequency, DateOffset alias, e.g. {‘T’ (minute), ‘H’, D’, ‘W’, ‘M’ (month end), ‘MS’ (month start), ‘Y’ (year end), ‘Y’ (year start)} See https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#offset-aliases

Type: str

anomaly_info¶

Anomaly adjustment info. Anomalies in df are corrected before any forecasting is done. See self.load_data()

Type: dict or list [dict] or None, default None

df_before_adjustment¶

self.df before adjustment by anomaly_info. Used by self.plot() to show the adjustment.

Type: pandas.DataFrame or None, default None

load_data(df: pandas.core.frame.DataFrame, time_col: str = 'ts', value_col: str = 'y', freq: str = None, date_format: str = None, tz: str = None, train_end_date: datetime.datetime = None, regressor_cols: List[str] = None, lagged_regressor_cols: List[str] = None, anomaly_info: Optional[Union[Dict, List[Dict]]] = None)[source]¶

Loads data to internal representation. Parses date column, sets timezone aware index. Checks for irregularities and raises an error if input is invalid. Adjusts for anomalies according to anomaly_info.

Parameters

df (pandas.DataFrame) – Input timeseries. A data frame which includes the timestamp column as well as the value column.
time_col (str) – The column name in df representing time for the time series data. The time column can be anything that can be parsed by pandas DatetimeIndex.
value_col (str) – The column name which has the value of interest to be forecasted.
freq (str or None, default None) – Timeseries frequency, DateOffset alias, If None automatically inferred.
date_format (str or None, default None) – strftime format to parse time column, eg %m/%d/%Y. Note that %f will parse all the way up to nanoseconds. If None (recommended), inferred by pandas.to_datetime.
tz (str or pytz.timezone object or None, default None) – Passed to pandas.tz_localize to localize the timestamp.
train_end_date (datetime.datetime or None, default None) – Last date to use for fitting the model. Forecasts are generated after this date. If None, it is set to the minimum of self.last_date_for_val and self.last_date_for_reg.
regressor_cols (list [str] or None, default None) – A list of regressor columns used in the training and prediction DataFrames. If None, no regressor columns are used. Regressor columns that are unavailable in df are dropped.
lagged_regressor_cols (list [str] or None, default None) – A list of additional columns needed for lagged regressors in the training and prediction DataFrames. This list can have overlap with regressor_cols. If None, no additional columns are added to the DataFrame. Lagged regressor columns that are unavailable in df are dropped.
anomaly_info (dict or list [dict] or None, default None) –
Anomaly adjustment info. Anomalies in df are corrected before any forecasting is done.

If None, no adjustments are made.

A dictionary containing the parameters to adjust_anomalous_data. See that function for details. The possible keys are:

"value_col"str
The name of the column in df to adjust. You may adjust the value to forecast as well as any numeric regressors.

"anomaly_df"pandas.DataFrame
Adjustments to correct the anomalies.

"start_date_col": str, default START_DATE_COL
Start date column in anomaly_df.

"end_date_col": str, default END_DATE_COL
End date column in anomaly_df.

"adjustment_delta_col": str or None, default None
Impact column in anomaly_df.

"filter_by_dict": dict or None, default None
Used to filter anomaly_df to the relevant anomalies for the value_col in this dictionary. Key specifies the column name, value specifies the filter value.

"filter_by_value_col"": str or None, default None
Adds {filter_by_value_col: value_col} to filter_by_dict if not None, for the value_col in this dictionary.

"adjustment_method"str (“add” or “subtract”), default “add”
How to make the adjustment, if adjustment_delta_col is provided.

Accepts a list of such dictionaries to adjust multiple columns in df.

Returns

self – Sets self.df with standard column names, value adjusted for anomalies, and time gaps filled in, sorted by time index.

Return type

Returns self.

describe_time_col()[source]¶

Basic descriptive stats on the timeseries time column.

Returns

time_stats –

Dictionary with descriptive stats on the timeseries time column.

data_points: int
number of time points

mean_increment_secs: float
mean frequency

min_timestamp: datetime64
start date

max_timestamp: datetime64
end date

Return type

dict

describe_value_col()[source]¶

Basic descriptive stats on the timeseries value column.

Returns: value_stats – Dict with keys: count, mean, std, min, 25%, 50%, 75%, max
Return type: dict [str, float]

make_future_dataframe(periods: int = None, include_history=True)[source]¶

Extends the input data for prediction into the future.

Includes the historical values (VALUE_COL) so this can be fed into a Pipeline that transforms input data for fitting, and for use in evaluation.

Parameters

periods (int or None) – Number of periods to forecast. If there are no regressors, default is 30. If there are regressors, default is to predict all available dates.
include_history (bool) – Whether to return historical dates and values with future dates.

Returns

future_df – Dataframe with future timestamps for prediction. Contains columns for:

prediction dates (TIME_COL),

values (VALUE_COL),

optional regressors

Return type

pandas.DataFrame

plot(color='rgb(32, 149, 212)', show_anomaly_adjustment=False, **kwargs)[source]¶

Returns interactive plotly graph of the value against time.

If anomaly info is provided, there is an option to show the anomaly adjustment.

Parameters

color (str, default “rgb(32, 149, 212)” (light blue)) – Color of the value line (after adjustment, if applicable).
show_anomaly_adjustment (bool, default False) – Whether to show the anomaly adjustment.
kwargs (additional parameters) – Additional parameters to pass to plot_univariate such as title and color.

Returns

fig – Interactive plotly graph of the value against time.

See plot_forecast_vs_actual return value for how to plot the figure and add customization.

Return type

plotly.graph_objects.Figure

get_grouping_evaluation(aggregation_func=<function nanmean>, aggregation_func_name='mean', groupby_time_feature=None, groupby_sliding_window_size=None, groupby_custom_column=None)[source]¶

Group-wise computation of aggregated timeSeries value. Can be used to evaluate error/ aggregated value by a time feature, over time, or by a user-provided column.

Exactly one of: groupby_time_feature, groupby_sliding_window_size, groupby_custom_column must be provided.

Parameters

aggregation_func (callable, optional, default numpy.nanmean) – Function that aggregates an array to a number. Signature (y: array) -> aggregated value: float.
aggregation_func_name (str or None, optional, default “mean”) – Name of grouping function, used to report results. If None, defaults to “aggregation”.
groupby_time_feature (str or None, optional) – If provided, groups by a column generated by build_time_features_df. See that function for valid values.
groupby_sliding_window_size (int or None, optional) – If provided, sequentially partitions data into groups of size groupby_sliding_window_size.
groupby_custom_column (pandas.Series or None, optional) – If provided, groups by this column value. Should be same length as the DataFrame.

Returns

grouped_df –

grouping_func_name: evaluation metric for aggregation of timeseries.
group name: group name depends on the grouping method: groupby_time_feature for groupby_time_feature cst.TIME_COL for groupby_sliding_window_size groupby_custom_column.name for groupby_custom_column.

Return type

pandas.DataFrame with two columns:

plot_grouping_evaluation(aggregation_func=<function nanmean>, aggregation_func_name='mean', groupby_time_feature=None, groupby_sliding_window_size=None, groupby_custom_column=None, xlabel=None, ylabel=None, title=None)[source]¶

Computes aggregated timeseries by group and plots the result. Can be used to plot aggregated timeseries by a time feature, over time, or by a user-provided column.

Exactly one of: groupby_time_feature, groupby_sliding_window_size, groupby_custom_column must be provided.

Parameters

aggregation_func (callable, optional, default numpy.nanmean) – Function that aggregates an array to a number. Signature (y: array) -> aggregated value: float.
aggregation_func_name (str or None, optional, default “mean”) – Name of grouping function, used to report results. If None, defaults to “aggregation”.
groupby_time_feature (str or None, optional) – If provided, groups by a column generated by build_time_features_df. See that function for valid values.
groupby_sliding_window_size (int or None, optional) – If provided, sequentially partitions data into groups of size groupby_sliding_window_size.
groupby_custom_column (pandas.Series or None, optional) – If provided, groups by this column value. Should be same length as the DataFrame.
xlabel (str, optional, default None) – X-axis label of the plot.
ylabel (str, optional, default None) – Y-axis label of the plot.
title (str or None, optional) – Plot title. If None, default is based on axis labels.

Returns

fig – plotly graph object showing aggregated timeseries by group. x-axis label depends on the grouping method: groupby_time_feature for groupby_time_feature TIME_COL for groupby_sliding_window_size groupby_custom_column.name for groupby_custom_column.

Return type

plotly.graph_objects.Figure

get_quantiles_and_overlays(groupby_time_feature=None, groupby_sliding_window_size=None, groupby_custom_column=None, show_mean=False, show_quantiles=False, show_overlays=False, overlay_label_time_feature=None, overlay_label_sliding_window_size=None, overlay_label_custom_column=None, center_values=False, value_col='y', mean_col_name='mean', quantile_col_prefix='Q', **overlay_pivot_table_kwargs)[source]¶

Computes mean, quantiles, and overlays by the requested grouping dimension.

Overlays are best explained in the plotting context. The grouping dimension goes on the x-axis, and one line is shown for each level of the overlay dimension. This function returns a column for each line to plot (e.g. mean, each quantile, each overlay value).

Exactly one of: groupby_time_feature, groupby_sliding_window_size, groupby_custom_column must be provided as the grouping dimension.

If show_overlays is True, exactly one of: overlay_label_time_feature, overlay_label_sliding_window_size, overlay_label_custom_column can be provided to specify the label_col (overlay dimension). Internally, the function calls pandas.DataFrame.pivot_table with index=groupby_col, columns=label_col, values=value_col to get the overlay values for plotting. You can pass additional parameters to pandas.DataFrame.pivot_table via overlay_pivot_table_kwargs, e.g. to change the aggregation method. If an explicit label is not provided, the records are labeled by their position within the group.

For example, to show yearly seasonality mean, quantiles, and overlay plots for each individual year, use:

self.get_quantiles_and_overlays(
    groupby_time_feature="doy",         # Rows: a row for each day of year (1, 2, ..., 366)
    show_mean=True,                     # mean value on that day
    show_quantiles=[0.1, 0.9],          # quantiles of the observed distribution on that day
    show_overlays=True,                 # Include overlays defined by ``overlay_label_time_feature``
    overlay_label_time_feature="year")  # One column for each observed "year" (2016, 2017, 2018, ...)

To show weekly seasonality over time, use:

self.get_quantiles_and_overlays(
    groupby_time_feature="dow",            # Rows: a row for each day of week (1, 2, ..., 7)
    show_mean=True,                        # mean value on that day
    show_quantiles=[0.1, 0.5, 0.9],        # quantiles of the observed distribution on that day
    show_overlays=True,                    # Include overlays defined by ``overlay_label_time_feature``
    overlay_label_sliding_window_size=90,  # One column for each 90 period sliding window in the dataset,
    aggfunc="median")                      # overlay value is the median value for the dow over the period (default="mean").

It may be difficult to assess the weekly seasonality from the previous result, because overlays shift up/down over time due to trend/yearly seasonality. Use center_values=True to adjust each overlay so its average value is centered at 0. Mean and quantiles are shifted by a single constant to center the mean at 0, while preserving their relative values:

self.get_quantiles_and_overlays(
    groupby_time_feature="dow",
    show_mean=True,
    show_quantiles=[0.1, 0.5, 0.9],
    show_overlays=True,
    overlay_label_sliding_window_size=90,
    aggfunc="median",
    center_values=True)  # Centers the output

Centering reduces the variability in the overlays to make it easier to isolate the effect by the groupby column. As a result, centered overlays have smaller variability than that reported by the quantiles, which operate on the original, uncentered data points. Similarly, if overlays are aggregates of individual values (i.e. aggfunc is needed in the call to pandas.DataFrame.pivot_table), the quantiles of overlays will be less extreme than those of the original data.

To assess variability conditioned on the groupby value, check the quantiles.

To assess variability conditioned on both the groupby and overlay value, after any necessary aggregation, check the variability of the overlay values. Compute quantiles of overlays from the return value if desired.

Parameters

groupby_time_feature (str or None, default None) – If provided, groups by a column generated by build_time_features_df. See that function for valid values.
groupby_sliding_window_size (int or None, default None) – If provided, sequentially partitions data into groups of size groupby_sliding_window_size.
groupby_custom_column (pandas.Series or None, default None) – If provided, groups by this column value. Should be same length as the DataFrame.
show_mean (bool, default False) – Whether to return the mean value by the groupby column.
show_quantiles (bool or list [float] or numpy.array, default False) – Whether to return the quantiles of the value by the groupby column. If False, does not return quantiles. If True, returns default quantiles (0.1 and 0.9). If array-like, a list of quantiles to compute (e.g. (0.1, 0.25, 0.75, 0.9)).
show_overlays (bool or int or array-like [int or str] or None, default False) –
Whether to return overlays of the value by the groupby column.

If False, no overlays are shown.

If True and label_col is defined, calls pandas.DataFrame.pivot_table with index=groupby_col, columns=label_col, values=value_col. label_col is defined by one of overlay_label_time_feature, overlay_label_sliding_window_size, or overlay_label_custom_column. Returns one column for each value of the label_col.

If True and the label_col is not defined, returns the raw values within each group. Values across groups are put into columns by their position in the group (1st element in group, 2nd, 3rd, etc.). Positional order in a group is not guaranteed to correspond to anything meaningful, so the items within a column may not have anything in common. It is better to specify one of overlay_* to explicitly define the overlay labels.

If an integer, the number of overlays to randomly sample. The same as True, then randomly samples up to int columns. This is useful if there are too many values.

If a list [int], a list of column indices (int type). The same as True, then selects the specified columns by index.

If a list [str], a list of column names. Column names are matched by their string representation to the names in this list. The same as True, then selects the specified columns by name.
overlay_label_time_feature (str or None, default None) –
If show_overlays is True, can be used to define label_col, i.e. which dimension to show separately as overlays.

If provided, uses a column generated by build_time_features_df. See that function for valid values.
overlay_label_sliding_window_size (int or None, default None) –
If show_overlays is True, can be used to define label_col, i.e. which dimension to show separately as overlays.

If provided, uses a column that sequentially partitions data into groups of size groupby_sliding_window_size.
overlay_label_custom_column (pandas.Series or None, default None) –
If show_overlays is True, can be used to define label_col, i.e. which dimension to show separately as overlays.

If provided, uses this column value. Should be same length as the DataFrame.
value_col (str, default VALUE_COL) – The column name for the value column. By default, shows the univariate time series value, but it can be any other column in self.df.
mean_col_name (str, default “mean”) – The name to use for the mean column in the output. Applies if show_mean=True.
quantile_col_prefix (str, default “Q”) – The prefix to use for quantile column names in the output. Columns are named with this prefix followed by the quantile, rounded to 2 decimal places.
center_values (bool, default False) –
Whether to center the return values. If True, shifts each overlay so its average value is centered at 0. Shifts mean and quantiles by a constant to center the mean at 0, while preserving their relative values.

If False, values are not centered.
overlay_pivot_table_kwargs (additional parameters) – Additional keyword parameters to pass to pandas.DataFrame.pivot_table, used in generating the overlays. See above description for details.

Returns

grouped_df – Dataframe with mean, quantiles, and overlays by the grouping column. Overlays are defined by the grouping column and overlay dimension.

ColumnIndex is a multiindex with first level as the “category”, a subset of [MEAN_COL_GROUP, QUANTILE_COL_GROUP, OVERLAY_COL_GROUP] depending on what is requests.

grouped_df[MEAN_COL_GROUP] = df with single column, named mean_col_name.

grouped_df[QUANTILE_COL_GROUP] = df with a column for each quantile, named f”{quantile_col_prefix}{round(str(q))}”, where q is the quantile.

grouped_df[OVERLAY_COL_GROUP] = df with one column per overlay value, named by the overlay value.

For example, it might look like:

category    mean    quantile        overlay
name        mean    Q0.1    Q0.9    2007    2008    2009
doy
1               8.42    7.72    9.08    8.29    7.75    8.33
2               8.82    8.20    9.56    8.43    8.80    8.53
3               8.95    8.25    9.88    8.26    9.12    8.70
4               9.07    8.60    9.49    8.10    9.99    8.73
5               8.73    8.29    9.24    7.95    9.26    8.37
...         ...     ...     ...     ...     ...     ...

Return type

pandas.DataFrame

plot_quantiles_and_overlays(groupby_time_feature=None, groupby_sliding_window_size=None, groupby_custom_column=None, show_mean=False, show_quantiles=False, show_overlays=False, overlay_label_time_feature=None, overlay_label_sliding_window_size=None, overlay_label_custom_column=None, center_values=False, value_col='y', mean_col_name='mean', quantile_col_prefix='Q', mean_style=None, quantile_style=None, overlay_style=None, xlabel=None, ylabel=None, title=None, showlegend=True, **overlay_pivot_table_kwargs)[source]¶

Plots mean, quantiles, and overlays by the requested grouping dimension.

The grouping dimension goes on the x-axis, and one line is shown for the mean, each quantile, and each level of the overlay dimension, as requested. By default, shading is applied between the quantiles.

Exactly one of: groupby_time_feature, groupby_sliding_window_size, groupby_custom_column must be provided as the grouping dimension.

If show_overlays is True, exactly one of: overlay_label_time_feature, overlay_label_sliding_window_size, overlay_label_custom_column can be provided to specify the label_col (overlay dimension). Internally, the function calls pandas.DataFrame.pivot_table with index=groupby_col, columns=label_col, values=value_col to get the overlay values for plotting. You can pass additional parameters to pandas.DataFrame.pivot_table via overlay_pivot_table_kwargs, e.g. to change the aggregation method. If an explicit label is not provided, the records are labeled by their position within the group.

For example, to show yearly seasonality mean, quantiles, and overlay plots for each individual year, use:

self.plot_quantiles_and_overlays(
    groupby_time_feature="doy",         # Rows: a row for each day of year (1, 2, ..., 366)
    show_mean=True,                     # mean value on that day
    show_quantiles=[0.1, 0.9],          # quantiles of the observed distribution on that day
    show_overlays=True,                 # Include overlays defined by ``overlay_label_time_feature``
    overlay_label_time_feature="year")  # One column for each observed "year" (2016, 2017, 2018, ...)

To show weekly seasonality over time, use:

self.plot_quantiles_and_overlays(
    groupby_time_feature="dow",            # Rows: a row for each day of week (1, 2, ..., 7)
    show_mean=True,                        # mean value on that day
    show_quantiles=[0.1, 0.5, 0.9],        # quantiles of the observed distribution on that day
    show_overlays=True,                    # Include overlays defined by ``overlay_label_time_feature``
    overlay_label_sliding_window_size=90,  # One column for each 90 period sliding window in the dataset,
    aggfunc="median")                      # overlay value is the median value for the dow over the period (default="mean").

It may be difficult to assess the weekly seasonality from the previous result, because overlays shift up/down over time due to trend/yearly seasonality. Use center_values=True to adjust each overlay so its average value is centered at 0. Mean and quantiles are shifted by a single constant to center the mean at 0, while preserving their relative values:

self.plot_quantiles_and_overlays(
    groupby_time_feature="dow",
    show_mean=True,
    show_quantiles=[0.1, 0.5, 0.9],
    show_overlays=True,
    overlay_label_sliding_window_size=90,
    aggfunc="median",
    center_values=True)  # Centers the output

Centering reduces the variability in the overlays to make it easier to isolate the effect by the groupby column. As a result, centered overlays have smaller variability than that reported by the quantiles, which operate on the original, uncentered data points. Similarly, if overlays are aggregates of individual values (i.e. aggfunc is needed in the call to pandas.DataFrame.pivot_table), the quantiles of overlays will be less extreme than those of the original data.

To assess variability conditioned on the groupby value, check the quantiles.

To assess variability conditioned on both the groupby and overlay value, after any necessary aggregation, check the variability of the overlay values. Compute quantiles of overlays from the return value if desired.

Parameters

groupby_time_feature (str or None, default None) – If provided, groups by a column generated by build_time_features_df. See that function for valid values.
groupby_sliding_window_size (int or None, default None) – If provided, sequentially partitions data into groups of size groupby_sliding_window_size.
groupby_custom_column (pandas.Series or None, default None) – If provided, groups by this column value. Should be same length as the DataFrame.
show_mean (bool, default False) – Whether to return the mean value by the groupby column.
show_quantiles (bool or list [float] or numpy.array, default False) – Whether to return the quantiles of the value by the groupby column. If False, does not return quantiles. If True, returns default quantiles (0.1 and 0.9). If array-like, a list of quantiles to compute (e.g. (0.1, 0.25, 0.75, 0.9)).
show_overlays (bool or int or array-like [int or str], default False) –
Whether to return overlays of the value by the groupby column.

If False, no overlays are shown.

If True and label_col is defined, calls pandas.DataFrame.pivot_table with index=groupby_col, columns=label_col, values=value_col. label_col is defined by one of overlay_label_time_feature, overlay_label_sliding_window_size, or overlay_label_custom_column. Returns one column for each value of the label_col.

If True and the label_col is not defined, returns the raw values within each group. Values across groups are put into columns by their position in the group (1st element in group, 2nd, 3rd, etc.). Positional order in a group is not guaranteed to correspond to anything meaningful, so the items within a column may not have anything in common. It is better to specify one of overlay_* to explicitly define the overlay labels.

If an integer, the number of overlays to randomly sample. The same as True, then randomly samples up to int columns. This is useful if there are too many values.

If a list [int], a list of column indices (int type). The same as True, then selects the specified columns by index.

If a list [str], a list of column names. Column names are matched by their string representation to the names in this list. The same as True, then selects the specified columns by name.
overlay_label_time_feature (str or None, default None) –
If show_overlays is True, can be used to define label_col, i.e. which dimension to show separately as overlays.

If provided, uses a column generated by build_time_features_df. See that function for valid values.
overlay_label_sliding_window_size (int or None, default None) –
If show_overlays is True, can be used to define label_col, i.e. which dimension to show separately as overlays.

If provided, uses a column that sequentially partitions data into groups of size groupby_sliding_window_size.
overlay_label_custom_column (pandas.Series or None, default None) –
If show_overlays is True, can be used to define label_col, i.e. which dimension to show separately as overlays.

If provided, uses this column value. Should be same length as the DataFrame.
value_col (str, default VALUE_COL) – The column name for the value column. By default, shows the univariate time series value, but it can be any other column in self.df.
mean_col_name (str, default “mean”) – The name to use for the mean column in the output. Applies if show_mean=True.
quantile_col_prefix (str, default “Q”) – The prefix to use for quantile column names in the output. Columns are named with this prefix followed by the quantile, rounded to 2 decimal places.
center_values (bool, default False) –
Whether to center the return values. If True, shifts each overlay so its average value is centered at 0. Shifts mean and quantiles by a constant to center the mean at 0, while preserving their relative values.

If False, values are not centered.

mean_style (dict or None, default None) –

How to style the mean line, passed as keyword arguments to plotly.graph_objects.Scatter. If None, the default is:

mean_style = {
    "line": dict(
        width=2,
        color="#595959"),  # gray
    "legendgroup": MEAN_COL_GROUP}

quantile_style (dict or None, default None) –
How to style the quantile lines, passed as keyword arguments to plotly.graph_objects.Scatter. If None, the default is:
```
quantile_style = {
    "line": dict(
        width=2,
        color="#1F9AFF",  # blue
        dash="solid"),
    "legendgroup": QUANTILE_COL_GROUP,  # show/hide them together
    "fill": "tonexty"}
```
Note that fill style is removed from to the first quantile line, to fill only between items in the same category.

overlay_style (dict or None, default None) –

How to style the overlay lines, passed as keyword arguments to plotly.graph_objects.Scatter. If None, the default is:

overlay_style = {
    "opacity": 0.5,  # makes it easier to see density
    "line": dict(
        width=1,
        color="#B3B3B3",  # light gray
        dash="solid"),
    "legendgroup": OVERLAY_COL_GROUP}

xlabel (str, optional, default None) – X-axis label of the plot.
ylabel (str, optional, default None) – Y-axis label of the plot. If None, uses value_col.
title (str or None, default None) – Plot title. If None, default is based on axis labels.
showlegend (bool, default True) – Whether to show the legend.
overlay_pivot_table_kwargs (additional parameters) – Additional keyword parameters to pass to pandas.DataFrame.pivot_table, used in generating the overlays. See get_quantiles_and_overlays description for details.

Returns

fig – plotly graph object showing the mean, quantiles, and overlays.

Return type

plotly.graph_objects.Figure

See also

None: To get the mean, quantiles, and overlays as a pandas.DataFrame without plotting.

class greykite.framework.output.univariate_forecast.UnivariateForecast(df, time_col='ts', actual_col='actual', predicted_col='forecast', predicted_lower_col='forecast_lower', predicted_upper_col='forecast_upper', null_model_predicted_col='forecast_null', ylabel='y', train_end_date=None, test_start_date=None, forecast_horizon=None, coverage=0.95, r2_loss_function=<function mean_squared_error>, estimator=None, relative_error_tolerance=None)[source]¶

Stores predicted and actual values. Provides functionality to evaluate a forecast:

plots true against actual with prediction bands.

evaluates model performance.

Input should be one of two kinds of forecast results:

model fit to train data, forecast on test set (actuals available).

model fit to all data, forecast on future dates (actuals not available).

The input df is a concatenation of fitted and forecasted values.

df¶

Timestamp, predicted, and actual values.

Type: pandas.DataFrame

time_col¶

Column in df with timestamp (default “ts”).

Type: str

actual_col¶

Column in df with actual values (default “y”).

Type: str

predicted_col¶

Column in df with predicted values (default “forecast”).

Type: str

predicted_lower_col¶

Column in df with predicted lower bound (default “forecast_lower”, optional).

Type: str or None

predicted_upper_col¶

Column in df with predicted upper bound (default “forecast_upper”, optional).

Type: str or None

null_model_predicted_col¶

Column in df with predicted value of null model (default “forecast_null”, optional).

Type: str or None

ylabel¶

Unit of measurement (default “y”)

Type: str

train_end_date¶

End date for train period. If None, assumes all data were used for training.

Type: str or datetime or None, default None

test_start_date¶

Start date of test period. If None, set to the time_col value immediately after train_end_date. This assumes that all data not used in training were used for testing.

Type: str or datetime or None, default None

forecast_horizon¶

Number of periods forecasted into the future. Must be > 0.

Type: int or None, default None

coverage¶

Intended coverage of the prediction bands (0.0 to 1.0).

Type: float or None

r2_loss_function¶

Loss function to calculate cst.R2_null_model_score, with signature loss_func(y_true, y_pred) (default mean_squared_error)

Type: function

estimator¶

The fitted estimator, the last step in the forecast pipeline.

Type: An instance of an estimator that implements greykite.models.base_forecast_estimator.BaseForecastEstimator.

relative_error_tolerance¶

Threshold to compute the Outside Tolerance metric, defined as the fraction of forecasted values whose relative error is strictly greater than relative_error_tolerance. For example, 0.05 allows for 5% relative error. If None, the metric is not computed.

Type: float or None, default None

df_train¶

Subset of df where df[time_col] <= train_end_date.

Type: pandas.DataFrame

df_test¶

Subset of df where df[time_col] > train_end_date.

Type: pandas.DataFrame

train_evaluation¶

Evaluation metrics on training set.

Type: dict [str, float]

test_evaluation¶

Evaluation metrics on test set (if actual values provided after train_end_date).

Type: dict [str, float]

test_na_count¶

Count of NA values in test data.

Type: int

compute_evaluation_metrics_split()[source]¶

Computes __evaluation_metrics for train and test set separately.

Returns: dictionary with train and test evaluation metrics

plot(**kwargs)[source]¶

Plots predicted against actual.

Parameters

kwargs (additional parameters) – Additional parameters to pass to plot_forecast_vs_actual such as title, colors, and line styling.

Returns

fig – Plotly figure of forecast against actuals, with prediction intervals if available.

See plot_forecast_vs_actual return value for how to plot the figure and add customization.

Return type

plotly.graph_objects.Figure

get_grouping_evaluation(score_func=<function add_finite_filter_to_scorer.<locals>.score_func_finite>, score_func_name='MAPE', which='train', groupby_time_feature=None, groupby_sliding_window_size=None, groupby_custom_column=None)[source]¶

Group-wise computation of forecasting error. Can be used to evaluate error/ aggregated value by a time feature, over time, or by a user-provided column.

Exactly one of: groupby_time_feature, groupby_sliding_window_size, groupby_custom_column must be provided.

Parameters

score_func (callable, optional) – Function that maps two arrays to a number. Signature (y_true: array, y_pred: array) -> error: float
score_func_name (str or None, optional) – Name of the score function used to report results. If None, defaults to “metric”.
which (str) – “train” or “test”. Which dataset to evaluate.
groupby_time_feature (str or None, optional) – If provided, groups by a column generated by build_time_features_df. See that function for valid values.
groupby_sliding_window_size (int or None, optional) – If provided, sequentially partitions data into groups of size groupby_sliding_window_size.
groupby_custom_column (pandas.Series or None, optional) – If provided, groups by this column value. Should be same length as the DataFrame.

Returns

grouped_df –

grouping_func_name: evaluation metric computing forecasting error of timeseries.
group name: group name depends on the grouping method: groupby_time_feature for groupby_time_feature cst.TIME_COL for groupby_sliding_window_size groupby_custom_column.name for groupby_custom_column.

Return type

pandas.DataFrame with two columns:

plot_grouping_evaluation(score_func=<function add_finite_filter_to_scorer.<locals>.score_func_finite>, score_func_name='MAPE', which='train', groupby_time_feature=None, groupby_sliding_window_size=None, groupby_custom_column=None, xlabel=None, ylabel=None, title=None)[source]¶

Computes error by group and plots the result. Can be used to plot error by a time feature, over time, or by a user-provided column.

Exactly one of: groupby_time_feature, groupby_sliding_window_size, groupby_custom_column must be provided.

Parameters

score_func (callable, optional) – Function that maps two arrays to a number. Signature (y_true: array, y_pred: array) -> error: float
score_func_name (str or None, optional) – Name of the score function used to report results. If None, defaults to “metric”.
which (str, optional, default “train”) – Which dataset to evaluate, “train” or “test”.
groupby_time_feature (str or None, optional) – If provided, groups by a column generated by build_time_features_df. See that function for valid values.
groupby_sliding_window_size (int or None, optional) – If provided, sequentially partitions data into groups of size groupby_sliding_window_size.
groupby_custom_column (pandas.Series or None, optional) – If provided, groups by this column value. Should be same length as the DataFrame.
xlabel (str, optional, default None) – X-axis label of the plot.
ylabel (str, optional, default None) – Y-axis label of the plot.
title (str or None, optional) – Plot title, if None this function creates a suitable title.

Returns

fig – plotly graph object showing forecasting error by group. x-axis label depends on the grouping method: groupby_time_feature for groupby_time_feature time_col for groupby_sliding_window_size groupby_custom_column.name for groupby_custom_column.

Return type

plotly.graph_objects.Figure

autocomplete_map_func_dict(map_func_dict)[source]¶

Sweeps through map_func_dict, converting values that are ElementwiseEvaluationMetricEnum member names to their corresponding row-wise evaluation function with appropriate column names for this UnivariateForecast instance.

For example:

map_func_dict = {
    "squared_error": ElementwiseEvaluationMetricEnum.SquaredError.name,
    "coverage": ElementwiseEvaluationMetricEnum.Coverage.name,
    "custom_metric": custom_function
}

is converted to

map_func_dict = {
    "squared_error": lambda row: ElementwiseEvaluationMetricEnum.SquaredError.get_metric_func()(
                                row[self.actual_col],
                                row[self.predicted_col]),
    "coverage": lambda row: ElementwiseEvaluationMetricEnum.Coverage.get_metric_func()(
                                row[self.actual_col],
                                row[self.predicted_lower_col],
                                row[self.predicted_upper_col]),
    "custom_metric": custom_function
}

Parameters: map_func_dict (dict or None) – Same as flexible_grouping_evaluation, with one exception: values may a ElementwiseEvaluationMetricEnum member name. There are converted a callable for flexible_grouping_evaluation.
Returns: map_func_dict – Can be passed to flexible_grouping_evaluation.
Return type: dict

get_flexible_grouping_evaluation(which='train', groupby_time_feature=None, groupby_sliding_window_size=None, groupby_custom_column=None, map_func_dict=None, agg_kwargs=None, extend_col_names=False)[source]¶

Group-wise computation of evaluation metrics. Whereas self.get_grouping_evaluation computes one metric, this allows computation of any number of custom metrics.

For example:

Mean and quantiles of squared error by group.

Mean and quantiles of residuals by group.

Mean and quantiles of actual and forecast by group.

% of actuals outside prediction intervals by group

any combination of the above metrics by the same group

First adds a groupby column by passing groupby_ parameters to add_groupby_column. Then computes grouped evaluation metrics by passing map_func_dict, agg_kwargs and extend_col_names to flexible_grouping_evaluation.

Exactly one of: groupby_time_feature, groupby_sliding_window_size, groupby_custom_column must be provided.

which: str

“train” or “test”. Which dataset to evaluate.

groupby_time_featurestr or None, optional

If provided, groups by a column generated by build_time_features_df. See that function for valid values.

groupby_sliding_window_sizeint or None, optional

If provided, sequentially partitions data into groups of size groupby_sliding_window_size.

groupby_custom_columnpandas.Series or None, optional

If provided, groups by this column value. Should be same length as the DataFrame.

map_func_dictdict [str, callable] or None, default None

Row-wise transformation functions to create new columns. If None, no new columns are added.

key: new column name

value: row-wise function to apply to df to generate the column value.
Signature (row: pandas.DataFrame) -> transformed value: float.

For example:

map_func_dict = {
    "residual": lambda row: row["actual"] - row["forecast"],
    "squared_error": lambda row: (row["actual"] - row["forecast"])**2
}

Some predefined functions are available in ElementwiseEvaluationMetricEnum. For example:

map_func_dict = {
    "residual": lambda row: ElementwiseEvaluationMetricEnum.Residual.get_metric_func()(
        row["actual"],
        row["forecast"]),
    "squared_error": lambda row: ElementwiseEvaluationMetricEnum.SquaredError.get_metric_func()(
        row["actual"],
        row["forecast"]),
    "q90_loss": lambda row: ElementwiseEvaluationMetricEnum.Quantile90.get_metric_func()(
        row["actual"],
        row["forecast"]),
    "abs_percent_error": lambda row: ElementwiseEvaluationMetricEnum.AbsolutePercentError.get_metric_func()(
        row["actual"],
        row["forecast"]),
    "coverage": lambda row: ElementwiseEvaluationMetricEnum.Coverage.get_metric_func()(
        row["actual"],
        row["forecast_lower"],
        row["forecast_upper"]),
}

As shorthand, it is sufficient to provide the enum member name. These are auto-expanded into the appropriate function. So the following is equivalent:

map_func_dict = {
    "residual": ElementwiseEvaluationMetricEnum.Residual.name,
    "squared_error": ElementwiseEvaluationMetricEnum.SquaredError.name,
    "q90_loss": ElementwiseEvaluationMetricEnum.Quantile90.name,
    "abs_percent_error": ElementwiseEvaluationMetricEnum.AbsolutePercentError.name,
    "coverage": ElementwiseEvaluationMetricEnum.Coverage.name,
}

agg_kwargsdict or None, default None

Passed as keyword args to pandas.core.groupby.DataFrameGroupBy.aggregate after creating new columns and grouping by groupby_col.

See pandas.core.groupby.DataFrameGroupBy.aggregate or flexible_grouping_evaluation for details.

extend_col_namesbool or None, default False

How to flatten index after aggregation. In some cases, the column index after aggregation is a multi-index. This parameter controls how to flatten an index with 2 levels to 1 level.

If None, the index is not flattened.

If True, column name is a composite: {index0}_{index1} Use this option if index1 is not unique.

If False, column name is simply {index1}

Ignored if the ColumnIndex after aggregation has only one level (e.g. if named aggregation is used in agg_kwargs).

Returns

df_transformed – df after transformation and optional aggregation.

If groupby_col is None, returns df with additional columns as the keys in map_func_dict. Otherwise, df is grouped by groupby_col and this becomes the index. Columns are determined by agg_kwargs and extend_col_names.

Return type

pandas.DataFrame

See also

None: called by this function
None: called by this function

plot_flexible_grouping_evaluation(which='train', groupby_time_feature=None, groupby_sliding_window_size=None, groupby_custom_column=None, map_func_dict=None, agg_kwargs=None, extend_col_names=False, y_col_style_dict='auto-fill', default_color='rgba(0, 145, 202, 1.0)', xlabel=None, ylabel=None, title=None, showlegend=True)[source]¶

Plots group-wise evaluation metrics. Whereas plot_grouping_evaluation shows one metric, this can show any number of custom metrics.

For example:

Mean and quantiles of squared error by group.

Mean and quantiles of residuals by group.

Mean and quantiles of actual and forecast by group.

% of actuals outside prediction intervals by group

any combination of the above metrics by the same group

See get_flexible_grouping_evaluation for details.

which: str

“train” or “test”. Which dataset to evaluate.

groupby_time_featurestr or None, optional

If provided, groups by a column generated by build_time_features_df. See that function for valid values.

groupby_sliding_window_sizeint or None, optional

If provided, sequentially partitions data into groups of size groupby_sliding_window_size.

groupby_custom_columnpandas.Series or None, optional

If provided, groups by this column value. Should be same length as the DataFrame.

map_func_dictdict [str, callable] or None, default None

Grouping evaluation metric specification, along with agg_kwargs. See get_flexible_grouping_evaluation.

agg_kwargsdict or None, default None

Grouping evaluation metric specification, along with map_func_dict. See get_flexible_grouping_evaluation.

extend_col_namesbool or None, default False

How to name the grouping metrics. See get_flexible_grouping_evaluation.

y_col_style_dict: dict [str, dict or None] or “plotly” or “auto” or “auto-fill”, default “auto-fill”

The column(s) to plot on the y-axis, and how to style them. The names should match those generated by agg_kwargs and extend_col_names. The function get_flexible_grouping_evaluation can be used to check the column names.

For convenience, start with “auto-fill” or “plotly”, then adjust styling as needed.

See plot_multivariate for details.

default_color: str, default “rgba(0, 145, 202, 1.0)” (blue)

Default line color when y_col_style_dict is one of “auto”, “auto-fill”.

xlabelstr or None, default None

x-axis label. If None, default is x_col.

ylabelstr or None, default None

y-axis label. If None, y-axis is not labeled.

titlestr or None, default None

Plot title. If None and ylabel is provided, a default title is used.

showlegendbool, default True

Whether to show the legend.

Returns

fig – Interactive plotly graph showing the evaluation metrics.

See plot_forecast_vs_actual return value for how to plot the figure and add customization.

Return type

plotly.graph_objects.Figure

See also

None: called by this function
None: called by this function

make_univariate_time_series()[source]¶

Converts prediction into a UnivariateTimeSeries Useful to convert a forecast into the input regressor for a subsequent forecast.

Returns: UnivariateTimeSeries

plot_components(**kwargs)[source]¶

Class method to plot the components of a UnivariateForecast object.

Silverkite calculates component plots based on fit dataset. Prophet calculates component plots based on predict dataset.

For estimator specific component plots with advanced plotting options call self.estimator.plot_components().

Returns: fig – matplotlib.figure.Figure for Prophet Figure plotting components against appropriate time scale.
Return type: plotly.graph_objects.Figure for Silverkite

class greykite.algo.common.model_summary.ModelSummary(x, y, pred_cols, pred_category, fit_algorithm, ml_model, max_colwidth=20)[source]¶

A class to store regression model summary statistics.

The class can be printed to get a well formatted model summary.

x¶

The design matrix.

Type: numpy.array

beta¶

The estimated coefficients.

Type: numpy.array

y¶

The response.

Type: numpy.array

pred_cols¶

List of predictor names.

Type: list [ str ]

pred_category¶

Predictor category, returned by create_pred_category.

Type: dict

fit_algorithm¶

The name of algorithm to fit the regression.

Type: str

ml_model¶

The trained machine learning model class.

Type: class

max_colwidth¶

The maximum length for predictors to be shown in their original name. If the maximum length of predictors exceeds this parameter, all predictors name will be suppressed and only indices are shown.

Type: int

info_dict¶

The model summary dictionary, output of _get_summary

Type: dict

__str__()[source]¶: print method.

__repr__()[source]¶: print method

_get_summary()[source]¶

Gets the model summary from input. This function is called during initialization.

Returns: info_dict – Includes direct and derived metrics about the trained model. For detailed keys, refer to get_info_dict_lm or get_info_dict_tree.
Return type: dict

get_coef_summary(is_intercept=None, is_time_feature=None, is_event=None, is_trend=None, is_seasonality=None, is_lag=None, is_regressor=None, is_interaction=None, return_df=False)[source]¶

Gets the coefficient summary filtered by conditions.

Parameters

is_intercept (bool or None, default None) – Intercept or not.
is_time_feature (bool or None, default None) – Time features or not. Time features belong to TIME_FEATURES.
is_event (bool or None, default None) – Event features or not. Event features have EVENT_PREFIX.
is_trend (bool or None, default None) – Trend features or not. Trend features have CHANGEPOINT_COL_PREFIX or “cpd”.
is_seasonality (bool or None, default None) – Seasonality feature or not. Seasonality features have SEASONALITY_REGEX.
is_lag (bool or None, default None) – Lagged features or not. Lagged features have “lag”.
is_regressor (0 or 1) – Extra features provided by users. They are provided through extra_pred_cols in the fit function.
is_interaction (bool or None, default None) – Interaction feature or not. Interaction features have “:”.
return_df (bool, default False) –

If True, the filtered coefficient summary df is also returned.
Otherwise, the filtered coefficient summary df is printed only.

Returns

filtered_coef_summary – If return_df is set to True, returns the filtered coefficient summary df filtered by the given conditions.

Return type

pandas.DataFrame or None

Constants¶

class greykite.common.evaluation.EvaluationMetricEnum(value)[source]¶

Valid evaluation metrics. The values tuple is (score_func: callable, greater_is_better: boolean, short_name: str)

add_finite_filter_to_scorer is added to the metrics that are directly imported from sklearn.metrics (e.g. mean_squared_error) to ensure that the metric gets calculated even when inputs have missing values.

Correlation = (<function add_finite_filter_to_scorer.<locals>.score_func_finite>, True, 'CORR')¶: Pearson correlation coefficient between forecast and actuals. Higher is better.

CoefficientOfDetermination = (<function add_finite_filter_to_scorer.<locals>.score_func_finite>, True, 'R2')¶: Coefficient of determination. See sklearn.metrics.r2_score. Higher is better. Equals 1.0 - mean_squared_error / variance(actuals).

MeanSquaredError = (<function add_finite_filter_to_scorer.<locals>.score_func_finite>, False, 'MSE')¶: Mean squared error, the average of squared differences, see sklearn.metrics.mean_squared_error.

RootMeanSquaredError = (<function add_finite_filter_to_scorer.<locals>.score_func_finite>, False, 'RMSE')¶: Root mean squared error, the square root of sklearn.metrics.mean_squared_error

MeanAbsoluteError = (<function add_finite_filter_to_scorer.<locals>.score_func_finite>, False, 'MAE')¶: Mean absolute error, average of absolute differences, see sklearn.metrics.mean_absolute_error.

MedianAbsoluteError = (<function add_finite_filter_to_scorer.<locals>.score_func_finite>, False, 'MedAE')¶: Median absolute error, median of absolute differences, see sklearn.metrics.median_absolute_error.

MeanAbsolutePercentError = (<function add_finite_filter_to_scorer.<locals>.score_func_finite>, False, 'MAPE')¶: Mean absolute percent error, error relative to actuals expressed as a %, see wikipedia MAPE.

MedianAbsolutePercentError = (<function add_finite_filter_to_scorer.<locals>.score_func_finite>, False, 'MedAPE')¶: Median absolute percent error, median of error relative to actuals expressed as a %, a median version of the MeanAbsolutePercentError, less affected by extreme values.

SymmetricMeanAbsolutePercentError = (<function add_finite_filter_to_scorer.<locals>.score_func_finite>, False, 'sMAPE')¶: Symmetric mean absolute percent error, error relative to (actuals+forecast) expressed as a %. Note that we do not include a factor of 2 in the denominator, so the range is 0% to 100%, see wikipedia sMAPE.

Quantile80 = (<function quantile_loss_q.<locals>.quantile_loss_wrapper>, False, 'Q80')¶

Quantile loss with q=0.80:

np.where(y_true < y_pred, (1 - q) * (y_pred - y_true), q * (y_true - y_pred)).mean()

Quantile95 = (<function quantile_loss_q.<locals>.quantile_loss_wrapper>, False, 'Q95')¶

Quantile loss with q=0.95:

np.where(y_true < y_pred, (1 - q) * (y_pred - y_true), q * (y_true - y_pred)).mean()

Quantile99 = (<function quantile_loss_q.<locals>.quantile_loss_wrapper>, False, 'Q99')¶

Quantile loss with q=0.99:

np.where(y_true < y_pred, (1 - q) * (y_pred - y_true), q * (y_true - y_pred)).mean()

FractionOutsideTolerance1 = (functools.partial(<function fraction_outside_tolerance>, rtol=0.01), False, 'OutsideTolerance1p')¶: Fraction of forecasted values that deviate more than 1% from the actual

FractionOutsideTolerance2 = (functools.partial(<function fraction_outside_tolerance>, rtol=0.02), False, 'OutsideTolerance2p')¶: Fraction of forecasted values that deviate more than 2% from the actual

FractionOutsideTolerance3 = (functools.partial(<function fraction_outside_tolerance>, rtol=0.03), False, 'OutsideTolerance3p')¶: Fraction of forecasted values that deviate more than 3% from the actual

FractionOutsideTolerance4 = (functools.partial(<function fraction_outside_tolerance>, rtol=0.04), False, 'OutsideTolerance4p')¶: Fraction of forecasted values that deviate more than 4% from the actual

FractionOutsideTolerance5 = (functools.partial(<function fraction_outside_tolerance>, rtol=0.05), False, 'OutsideTolerance5p')¶: Fraction of forecasted values that deviate more than 5% from the actual

get_metric_func()[source]¶: Returns the metric function

get_metric_greater_is_better()[source]¶: Returns the greater_is_better boolean

get_metric_name()[source]¶: Returns the short name

Constants used by code in common or in multiple places: algo, sklearn, and/or framework.

greykite.common.constants.TIME_COL = 'ts'¶: The default name for the column with the timestamps of the time series

greykite.common.constants.VALUE_COL = 'y'¶: The default name for the column with the values of the time series

greykite.common.constants.ACTUAL_COL = 'actual'¶: The column name representing actual (observed) values

greykite.common.constants.PREDICTED_COL = 'forecast'¶: The column name representing the predicted values

greykite.common.constants.PREDICTED_LOWER_COL = 'forecast_lower'¶: The column name representing upper bounds of prediction interval

greykite.common.constants.PREDICTED_UPPER_COL = 'forecast_upper'¶: The column name representing lower bounds of prediction interval

greykite.common.constants.NULL_PREDICTED_COL = 'forecast_null'¶: The column name representing predicted values from null model

greykite.common.constants.ERR_STD_COL = 'err_std'¶: The column name representing the error standard deviation from models

greykite.common.constants.R2_null_model_score = 'R2_null_model_score'¶: Evaluation metric. Improvement in the specified loss function compared to the predictions of a null model.

greykite.common.constants.FRACTION_OUTSIDE_TOLERANCE = 'Outside Tolerance (fraction)'¶: Evaluation metric. The fraction of predictions outside the specified tolerance level

greykite.common.constants.PREDICTION_BAND_WIDTH = 'Prediction Band Width (%)'¶: Evaluation metric. Relative size of prediction bands vs actual, as a percent

greykite.common.constants.PREDICTION_BAND_COVERAGE = 'Prediction Band Coverage (fraction)'¶: Evaluation metric. Fraction of observations within the bands

greykite.common.constants.LOWER_BAND_COVERAGE = 'Coverage: Lower Band'¶: Evaluation metric. Fraction of observations within the lower band

greykite.common.constants.UPPER_BAND_COVERAGE = 'Coverage: Upper Band'¶: Evaluation metric. Fraction of observations within the upper band

greykite.common.constants.COVERAGE_VS_INTENDED_DIFF = 'Coverage Diff: Actual_Coverage - Intended_Coverage'¶: Evaluation metric. Difference between actual and intended coverage

greykite.common.constants.EVENT_DF_DATE_COL = 'date'¶: Name of date column for the DataFrames passed to silverkite custom_daily_event_df_dict

greykite.common.constants.EVENT_DF_LABEL_COL = 'event_name'¶: Name of event column for the DataFrames passed to silverkite custom_daily_event_df_dict

greykite.common.constants.EVENT_PREFIX = 'events'¶: Prefix for naming event features.

greykite.common.constants.EVENT_DEFAULT = ''¶: Label used for days without an event.

greykite.common.constants.EVENT_INDICATOR = 'event'¶: Binary indicatory for an event

greykite.common.constants.CHANGEPOINT_COL_PREFIX = 'changepoint'¶: Prefix for naming changepoint features.

greykite.common.constants.CHANGEPOINT_COL_PREFIX_SHORT = 'cp'¶: Short prefix for naming changepoint features.

greykite.common.constants.START_DATE_COL = 'start_date'¶: Start timestamp column name

greykite.common.constants.END_DATE_COL = 'end_date'¶: Standard end timestamp column

greykite.common.constants.ADJUSTMENT_DELTA_COL = 'adjustment_delta'¶: Adjustment column

greykite.common.constants.METRIC_COL = 'metric'¶: Column to denote metric of interest

greykite.common.constants.DIMENSION_COL = 'dimension'¶: Dimension column

greykite.common.constants.GROWTH_COL_ALIAS = {'cuberoot': 'ct_root3', 'cubic': 'ct3', 'linear': 'ct1', 'quadratic': 'ct2', 'sqrt': 'ct_sqrt'}¶: Human-readable names for the growth columns generated by build_time_features_df

greykite.common.constants.TIME_FEATURES = ['datetime', 'date', 'year', 'year_length', 'quarter', 'quarter_start', 'quarter_length', 'month', 'month_length', 'woy', 'doy', 'doq', 'dom', 'dow', 'str_dow', 'str_doy', 'hour', 'minute', 'second', 'year_month', 'year_woy', 'month_dom', 'year_woy_dow', 'woy_dow', 'dow_hr', 'dow_hr_min', 'tod', 'tow', 'tom', 'toq', 'toy', 'conti_year', 'is_weekend', 'dow_grouped', 'ct1', 'ct2', 'ct3', 'ct_sqrt', 'ct_root3']¶: Time features generated by build_time_features_df

greykite.common.constants.LAG_INFIX = '_lag'¶: Infix for lagged feature names

greykite.common.constants.AGG_LAG_INFIX = 'avglag'¶: Infix for aggregated lag feature names

greykite.common.constants.TREND_REGEX = 'changepoint\\d|ct\\d|ct_|cp\\d'¶: Growth terms, including changepoints.

greykite.common.constants.SEASONALITY_REGEX = 'sin\\d|cos\\d'¶: Seasonality terms modeled by fourier series.

greykite.common.constants.EVENT_REGEX = 'events_'¶: Event terms.

greykite.common.constants.LAG_REGEX = '_lag\\d|_avglag_\\d'¶: Lag terms.

greykite.common.constants.LOGGER_NAME = 'Greykite'¶: Name used by the logger.

Constants used by `~greykite.framework.

greykite.framework.constants.EVALUATION_PERIOD_CV_MAX_SPLITS = 3¶: Default value for EvaluationPeriodParam().cv_max_splits

greykite.framework.constants.COMPUTATION_N_JOBS = 1¶: Default value for ComputationParam.n_jobs

greykite.framework.constants.COMPUTATION_VERBOSE = 1¶: Default value for ComputationParam.verbose

greykite.framework.constants.CV_REPORT_METRICS_ALL = 'ALL'¶: Set cv_report_metrics to this value to compute all metrics during CV

greykite.framework.constants.FRACTION_OUTSIDE_TOLERANCE_NAME = 'OutsideTolerance'¶: Short name used to report the result of FRACTION_OUTSIDE_TOLERANCE in CV

greykite.framework.constants.CUSTOM_SCORE_FUNC_NAME = 'score'¶: Short name used to report the result of custom score_func in CV

greykite.framework.constants.MEAN_COL_GROUP = 'mean'¶: Columns with mean.

greykite.framework.constants.QUANTILE_COL_GROUP = 'quantile'¶: Columns with quantile.

greykite.framework.constants.OVERLAY_COL_GROUP = 'overlay'¶: Columns with overlay.

greykite.framework.constants.FORECAST_STEP_COL = 'forecast_step'¶: The column name for forecast step in benchmarking

class greykite.algo.forecast.silverkite.constants.silverkite_constant.SilverkiteConstant[source]¶

Uses the appropriate constant mixins to provide all the constants that will be used by Silverkite.

get_silverkite_column() → Type[greykite.algo.forecast.silverkite.constants.silverkite_column.SilverkiteColumn]¶: Return the SilverkiteColumn constants

get_silverkite_components_enum() → Type[greykite.algo.forecast.silverkite.constants.silverkite_component.SilverkiteComponentsEnum]¶: Return the SilverkiteComponentsEnum constants

get_silverkite_holiday() → Type[greykite.algo.forecast.silverkite.constants.silverkite_holiday.SilverkiteHoliday]¶: Return the SilverkiteHoliday constants

get_silverkite_seasonality_enum() → Type[greykite.algo.forecast.silverkite.constants.silverkite_seasonality.SilverkiteSeasonalityEnum]¶: Return the SilverkiteSeasonalityEnum constants

get_silverkite_time_frequency_enum() → Type[greykite.algo.forecast.silverkite.constants.silverkite_time_frequency.SilverkiteTimeFrequencyEnum]¶: Return the SilverkiteTimeFrequencyEnum constants

class greykite.algo.forecast.silverkite.constants.silverkite_column.SilverkiteColumn[source]¶

Silverkite feature sets for sub-daily data.

COLS_HOUR_OF_WEEK: str = 'hour_of_week'¶: Silverkite feature_sets_enabled key. constant hour of week effect

COLS_WEEKEND_SEAS: str = 'is_weekend:daily_seas'¶: Silverkite feature_sets_enabled key. daily seasonality interaction with is_weekend

COLS_DAY_OF_WEEK_SEAS: str = 'day_of_week:daily_seas'¶: Silverkite feature_sets_enabled key. daily seasonality interaction with day of week

COLS_TREND_DAILY_SEAS: str = 'trend:is_weekend:daily_seas'¶: Silverkite feature_sets_enabled key. allow daily seasonality to change over time, depending on is_weekend

COLS_EVENT_SEAS: str = 'event:daily_seas'¶: Silverkite feature_sets_enabled key. allow sub-daily event effects

COLS_EVENT_WEEKEND_SEAS: str = 'event:is_weekend:daily_seas'¶: Silverkite feature_sets_enabled key. allow sub-daily event effect to interact with is_weekend

COLS_DAY_OF_WEEK: str = 'day_of_week'¶: Silverkite feature_sets_enabled key. constant day of week effect

COLS_TREND_WEEKEND: str = 'trend:is_weekend'¶: Silverkite feature_sets_enabled key. allow trend (growth, changepoints) to interact with is_weekend

COLS_TREND_DAY_OF_WEEK: str = 'trend:day_of_week'¶: Silverkite feature_sets_enabled key. allow trend to interact with day of week

COLS_TREND_WEEKLY_SEAS: str = 'trend:weekly_seas'¶: Silverkite feature_sets_enabled key. allow weekly seasonality to change over time

class greykite.algo.forecast.silverkite.constants.silverkite_component.SilverkiteComponentsEnum(value)[source]¶: Defines groupby time feature, xlabel and ylabel for Silverkite Component Plots.

class greykite.algo.forecast.silverkite.constants.silverkite_holiday.SilverkiteHoliday[source]¶

Holiday constants to be used by Silverkite

HOLIDAY_LOOKUP_COUNTRIES_AUTO = ('UnitedStates', 'UnitedKingdom', 'India', 'France', 'China')¶: Auto setting for the countries that contain the holidays to include in the model

HOLIDAYS_TO_MODEL_SEPARATELY_AUTO = ("New Year's Day", 'Chinese New Year', 'Christmas Day', 'Independence Day', 'Thanksgiving', 'Labor Day', 'Good Friday', 'Easter Monday [England, Wales, Northern Ireland]', 'Memorial Day', 'Veterans Day')¶: Auto setting for the holidays to include in the model

ALL_HOLIDAYS_IN_COUNTRIES = 'ALL_HOLIDAYS_IN_COUNTRIES'¶: Value for holidays_to_model_separately to request all holidays in the lookup countries

HOLIDAYS_TO_INTERACT = ('Christmas Day', 'Christmas Day_minus_1', 'Christmas Day_minus_2', 'Christmas Day_plus_1', 'Christmas Day_plus_2', 'New Years Day', 'New Years Day_minus_1', 'New Years Day_minus_2', 'New Years Day_plus_1', 'New Years Day_plus_2', 'Thanksgiving', 'Thanksgiving_plus_1', 'Independence Day')¶: Significant holidays that may have a different daily seasonality pattern

class greykite.algo.forecast.silverkite.constants.silverkite_seasonality.SilverkiteSeasonalityEnum(value)[source]¶

Defines default seasonalities for Silverkite estimator. Names should match those in SeasonalityEnum. The default order for various seasonalities is stored in this enum.

DAILY_SEASONALITY: greykite.algo.forecast.silverkite.constants.silverkite_seasonality.SilverkiteSeasonality = SilverkiteSeasonality(name='tod', period=24.0, order=12, seas_names='daily', default_min_days=2)¶: tod is 0-24 time of day (tod granularity based on input data, up to second level). Requires at least two full cycles to add the seasonal term (default_min_days=2).

WEEKLY_SEASONALITY: greykite.algo.forecast.silverkite.constants.silverkite_seasonality.SilverkiteSeasonality = SilverkiteSeasonality(name='tow', period=7.0, order=4, seas_names='weekly', default_min_days=14)¶: tow is 0-7 time of week (tow granularity based on input data, up to second level). order=4 for full flexibility to model daily input.

MONTHLY_SEASONALITY: greykite.algo.forecast.silverkite.constants.silverkite_seasonality.SilverkiteSeasonality = SilverkiteSeasonality(name='tom', period=1.0, order=2, seas_names='monthly', default_min_days=60)¶: tom is 0-1 time of month (tom granularity based on input data, up to daily level).

QUARTERLY_SEASONALITY: greykite.algo.forecast.silverkite.constants.silverkite_seasonality.SilverkiteSeasonality = SilverkiteSeasonality(name='toq', period=1.0, order=5, seas_names='quarterly', default_min_days=180)¶: toq (continuous time of quarter) with natural period. Each day is mapped to a value in [0.0, 1.0) based on its position in the calendar quarter: (Jan1-Mar31, Apr1-Jun30, Jul1-Sep30, Oct1-Dec31). The start of each quarter is 0.0.

YEARLY_SEASONALITY: greykite.algo.forecast.silverkite.constants.silverkite_seasonality.SilverkiteSeasonality = SilverkiteSeasonality(name='ct1', period=1.0, order=15, seas_names='yearly', default_min_days=548)¶: ct1 (continuous year) with natural period.

class greykite.algo.forecast.silverkite.constants.silverkite_time_frequency.SilverkiteTimeFrequencyEnum(value)[source]¶: Provides properties for modeling for various time frequencies in Silverkite. The enum names is the time frequency, corresponding to the simple time frequencies in SimpleTimeFrequencyEnum.

Provides templates for SimpleSilverkiteEstimator that are pre-tuned to fit specific use cases.

A subset of these templates are recognized by ModelTemplateEnum.

simple_silverkite_template also accepts any model_template name that follows the naming convention in this file. For details, see the model_template parameter in SimpleSilverkiteTemplate.

class greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_FREQ(value)[source]¶: Valid values for simple silverkite template string name frequency.

greykite.framework.templates.simple_silverkite_template_config.VALID_FREQ = ['HOURLY', 'DAILY', 'WEEKLY']¶: Valid non-default values for simple silverkite template string name frequency. These are the non-default frequencies recognized by SimpleSilverkiteTemplateOptions.

class greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_SEAS(value)[source]¶: Valid values for simple silverkite template string name seasonality.

class greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_GR(value)[source]¶: Valid values for simple silverkite template string name growth_term.

class greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_CP(value)[source]¶: Valid values for simple silverkite template string name changepoints_dict.

class greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_HOL(value)[source]¶: Valid values for simple silverkite template string name events.

class greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_FEASET(value)[source]¶: Valid values for simple silverkite template string name feature_sets_enabled.

class greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_ALGO(value)[source]¶: Valid values for simple silverkite template string name fit_algorithm.

class greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_AR(value)[source]¶: Valid values for simple silverkite template string name autoregression.

class greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_DSI(value)[source]¶: Valid values for simple silverkite template string name daily seasonality max interaction order.

class greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_WSI(value)[source]¶: Valid values for simple silverkite template string name weekly seasonality max interaction order.

class greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_COMPONENT_KEYWORDS(value)[source]¶: Valid values for simple silverkite template string name keywords. The names are the keywords and the values are the corresponding value enum. Can be used to create an instance of SimpleSilverkiteTemplateOptions.

class greykite.framework.templates.simple_silverkite_template_config.SimpleSilverkiteTemplateOptions(freq: greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_FREQ = <SILVERKITE_FREQ.DAILY: 'DAILY'>, seas: greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_SEAS = <SILVERKITE_SEAS.LT: 'LT'>, gr: greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_GR = <SILVERKITE_GR.LINEAR: 'LINEAR'>, cp: greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_CP = <SILVERKITE_CP.NONE: 'NONE'>, hol: greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_HOL = <SILVERKITE_HOL.NONE: 'NONE'>, feaset: greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_FEASET = <SILVERKITE_FEASET.OFF: 'OFF'>, algo: greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_ALGO = <SILVERKITE_ALGO.LINEAR: 'LINEAR'>, ar: greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_AR = <SILVERKITE_AR.OFF: 'OFF'>, dsi: greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_DSI = <SILVERKITE_DSI.AUTO: 'AUTO'>, wsi: greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_WSI = <SILVERKITE_WSI.AUTO: 'AUTO'>)[source]¶

Defines generic simple silverkite template options.

Attributes can be set to different values using SILVERKITE_COMPONENT_KEYWORDS for high level tuning.

freq represents data frequency.

The other attributes stand for seasonality, growth, changepoints_dict, events, feature_sets_enabled, fit_algorithm and autoregression in ModelComponentsParam, which are used in SimpleSilverkiteTemplate.

freq: greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_FREQ = 'DAILY'¶: Valid values for simple silverkite template string name frequency. See SILVERKITE_FREQ.

seas: greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_SEAS = 'LT'¶: Valid values for simple silverkite template string name seasonality. See SILVERKITE_SEAS.

gr: greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_GR = 'LINEAR'¶: Valid values for simple silverkite template string name growth. See SILVERKITE_GR.

cp: greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_CP = 'NONE'¶: Valid values for simple silverkite template string name changepoints. See SILVERKITE_CP.

hol: greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_HOL = 'NONE'¶: Valid values for simple silverkite template string name holiday. See SILVERKITE_HOL.

feaset: greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_FEASET = 'OFF'¶: Valid values for simple silverkite template string name feature sets enabled. See SILVERKITE_FEASET.

algo: greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_ALGO = 'LINEAR'¶: Valid values for simple silverkite template string name fit algorithm. See SILVERKITE_ALGO.

ar: greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_AR = 'OFF'¶: Valid values for simple silverkite template string name autoregression. See SILVERKITE_AR.

dsi: greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_DSI = 'AUTO'¶: Valid values for simple silverkite template string name max daily seasonality interaction order. See SILVERKITE_DSI.

wsi: greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_WSI = 'AUTO'¶: Valid values for simple silverkite template string name max weekly seasonality interaction order. See SILVERKITE_WSI.

greykite.framework.templates.simple_silverkite_template_config.COMMON_MODELCOMPONENTPARAM_PARAMETERS = {'ALGO': {'LASSO': {'fit_algorithm': 'lasso', 'fit_algorithm_params': None}, 'LINEAR': {'fit_algorithm': 'linear', 'fit_algorithm_params': None}, 'RIDGE': {'fit_algorithm': 'ridge', 'fit_algorithm_params': None}, 'SGD': {'fit_algorithm': 'sgd', 'fit_algorithm_params': None}}, 'AR': {'AUTO': {'autoreg_dict': 'auto', 'simulation_num': 10}, 'OFF': {'autoreg_dict': None, 'simulation_num': 10}}, 'CP': {'DAILY': {'HV': {'method': 'auto', 'no_changepoint_distance_from_end': '180D', 'potential_changepoint_distance': '15D', 'regularization_strength': 0.3, 'resample_freq': '7D', 'yearly_seasonality_change_freq': '365D', 'yearly_seasonality_order': 15}, 'LT': {'method': 'auto', 'no_changepoint_distance_from_end': '90D', 'potential_changepoint_distance': '15D', 'regularization_strength': 0.6, 'resample_freq': '7D', 'yearly_seasonality_change_freq': None, 'yearly_seasonality_order': 15}, 'NM': {'method': 'auto', 'no_changepoint_distance_from_end': '180D', 'potential_changepoint_distance': '15D', 'regularization_strength': 0.5, 'resample_freq': '7D', 'yearly_seasonality_change_freq': '365D', 'yearly_seasonality_order': 15}, 'NONE': None}, 'HOURLY': {'HV': {'method': 'auto', 'no_changepoint_distance_from_end': '30D', 'potential_changepoint_distance': '15D', 'regularization_strength': 0.3, 'resample_freq': 'D', 'yearly_seasonality_change_freq': '365D', 'yearly_seasonality_order': 15}, 'LT': {'method': 'auto', 'no_changepoint_distance_from_end': '30D', 'potential_changepoint_distance': '7D', 'regularization_strength': 0.6, 'resample_freq': 'D', 'yearly_seasonality_change_freq': None, 'yearly_seasonality_order': 15}, 'NM': {'method': 'auto', 'no_changepoint_distance_from_end': '30D', 'potential_changepoint_distance': '15D', 'regularization_strength': 0.5, 'resample_freq': 'D', 'yearly_seasonality_change_freq': '365D', 'yearly_seasonality_order': 15}, 'NONE': None}, 'WEEKLY': {'HV': {'method': 'auto', 'no_changepoint_distance_from_end': '180D', 'potential_changepoint_distance': '14D', 'regularization_strength': 0.3, 'resample_freq': '7D', 'yearly_seasonality_change_freq': '365D', 'yearly_seasonality_order': 15}, 'LT': {'method': 'auto', 'no_changepoint_distance_from_end': '180D', 'potential_changepoint_distance': '14D', 'regularization_strength': 0.6, 'resample_freq': '7D', 'yearly_seasonality_change_freq': None, 'yearly_seasonality_order': 15}, 'NM': {'method': 'auto', 'no_changepoint_distance_from_end': '180D', 'potential_changepoint_distance': '14D', 'regularization_strength': 0.5, 'resample_freq': '7D', 'yearly_seasonality_change_freq': '365D', 'yearly_seasonality_order': 15}, 'NONE': None}}, 'DSI': {'DAILY': {'AUTO': 0, 'OFF': 0}, 'HOURLY': {'AUTO': 5, 'OFF': 0}, 'WEEKLY': {'AUTO': 0, 'OFF': 0}}, 'FEASET': {'AUTO': 'auto', 'OFF': False, 'ON': True}, 'GR': {'LINEAR': {'growth_term': 'linear'}, 'NONE': {'growth_term': None}}, 'HOL': {'NONE': {'daily_event_df_dict': None, 'holiday_lookup_countries': [], 'holiday_post_num_days': 0, 'holiday_pre_num_days': 0, 'holiday_pre_post_num_dict': None, 'holidays_to_model_separately': []}, 'SP1': {'daily_event_df_dict': None, 'holiday_lookup_countries': 'auto', 'holiday_post_num_days': 1, 'holiday_pre_num_days': 1, 'holiday_pre_post_num_dict': None, 'holidays_to_model_separately': 'auto'}, 'SP2': {'daily_event_df_dict': None, 'holiday_lookup_countries': 'auto', 'holiday_post_num_days': 2, 'holiday_pre_num_days': 2, 'holiday_pre_post_num_dict': None, 'holidays_to_model_separately': 'auto'}, 'SP4': {'daily_event_df_dict': None, 'holiday_lookup_countries': 'auto', 'holiday_post_num_days': 4, 'holiday_pre_num_days': 4, 'holiday_pre_post_num_dict': None, 'holidays_to_model_separately': 'auto'}, 'TG': {'daily_event_df_dict': None, 'holiday_lookup_countries': 'auto', 'holiday_post_num_days': 3, 'holiday_pre_num_days': 3, 'holiday_pre_post_num_dict': None, 'holidays_to_model_separately': []}}, 'SEAS': {'DAILY': {'HV': {'daily_seasonality': 0, 'monthly_seasonality': 0, 'quarterly_seasonality': 0, 'weekly_seasonality': 4, 'yearly_seasonality': 25}, 'HVQM': {'daily_seasonality': 0, 'monthly_seasonality': 4, 'quarterly_seasonality': 6, 'weekly_seasonality': 4, 'yearly_seasonality': 25}, 'LT': {'daily_seasonality': 0, 'monthly_seasonality': 0, 'quarterly_seasonality': 0, 'weekly_seasonality': 3, 'yearly_seasonality': 8}, 'LTQM': {'daily_seasonality': 0, 'monthly_seasonality': 2, 'quarterly_seasonality': 3, 'weekly_seasonality': 3, 'yearly_seasonality': 8}, 'NM': {'daily_seasonality': 0, 'monthly_seasonality': 0, 'quarterly_seasonality': 0, 'weekly_seasonality': 3, 'yearly_seasonality': 15}, 'NMQM': {'daily_seasonality': 0, 'monthly_seasonality': 4, 'quarterly_seasonality': 4, 'weekly_seasonality': 3, 'yearly_seasonality': 15}, 'NONE': {'daily_seasonality': 0, 'monthly_seasonality': 0, 'quarterly_seasonality': 0, 'weekly_seasonality': 0, 'yearly_seasonality': 0}}, 'HOURLY': {'HV': {'daily_seasonality': 12, 'monthly_seasonality': 0, 'quarterly_seasonality': 0, 'weekly_seasonality': 6, 'yearly_seasonality': 25}, 'HVQM': {'daily_seasonality': 12, 'monthly_seasonality': 4, 'quarterly_seasonality': 4, 'weekly_seasonality': 6, 'yearly_seasonality': 25}, 'LT': {'daily_seasonality': 5, 'monthly_seasonality': 0, 'quarterly_seasonality': 0, 'weekly_seasonality': 3, 'yearly_seasonality': 8}, 'LTQM': {'daily_seasonality': 5, 'monthly_seasonality': 2, 'quarterly_seasonality': 2, 'weekly_seasonality': 3, 'yearly_seasonality': 8}, 'NM': {'daily_seasonality': 8, 'monthly_seasonality': 0, 'quarterly_seasonality': 0, 'weekly_seasonality': 4, 'yearly_seasonality': 15}, 'NMQM': {'daily_seasonality': 8, 'monthly_seasonality': 3, 'quarterly_seasonality': 3, 'weekly_seasonality': 4, 'yearly_seasonality': 15}, 'NONE': {'daily_seasonality': 0, 'monthly_seasonality': 0, 'quarterly_seasonality': 0, 'weekly_seasonality': 0, 'yearly_seasonality': 0}}, 'WEEKLY': {'HV': {'daily_seasonality': 0, 'monthly_seasonality': 0, 'quarterly_seasonality': 0, 'weekly_seasonality': 0, 'yearly_seasonality': 25}, 'HVQM': {'daily_seasonality': 0, 'monthly_seasonality': 4, 'quarterly_seasonality': 4, 'weekly_seasonality': 0, 'yearly_seasonality': 25}, 'LT': {'daily_seasonality': 0, 'monthly_seasonality': 0, 'quarterly_seasonality': 0, 'weekly_seasonality': 0, 'yearly_seasonality': 8}, 'LTQM': {'daily_seasonality': 0, 'monthly_seasonality': 2, 'quarterly_seasonality': 2, 'weekly_seasonality': 0, 'yearly_seasonality': 8}, 'NM': {'daily_seasonality': 0, 'monthly_seasonality': 0, 'quarterly_seasonality': 0, 'weekly_seasonality': 0, 'yearly_seasonality': 15}, 'NMQM': {'daily_seasonality': 0, 'monthly_seasonality': 3, 'quarterly_seasonality': 3, 'weekly_seasonality': 0, 'yearly_seasonality': 15}, 'NONE': {'daily_seasonality': 0, 'monthly_seasonality': 0, 'quarterly_seasonality': 0, 'weekly_seasonality': 0, 'yearly_seasonality': 0}}}, 'WSI': {'DAILY': {'AUTO': 2, 'OFF': 0}, 'HOURLY': {'AUTO': 2, 'OFF': 0}, 'WEEKLY': {'AUTO': 0, 'OFF': 0}}}¶: Defines the default component values for SimpleSilverkiteTemplate. The components include seasonality, growth, holiday, trend changepoints, feature sets, autoregression, fit algorithm, etc. These are used when config.model_template provides the SimpleSilverkiteTemplateOptions.

greykite.framework.templates.simple_silverkite_template_config.SILVERKITE = ModelComponentsParam(autoregression={'autoreg_dict': None, 'simulation_num': 10}, changepoints={'changepoints_dict': None, 'seasonality_changepoints_dict': None}, custom={'fit_algorithm_dict': {'fit_algorithm': 'ridge', 'fit_algorithm_params': None}, 'feature_sets_enabled': 'auto', 'max_daily_seas_interaction_order': 5, 'max_weekly_seas_interaction_order': 2, 'extra_pred_cols': [], 'drop_pred_cols': None, 'explicit_pred_cols': None, 'min_admissible_value': None, 'max_admissible_value': None, 'regression_weight_col': None, 'normalize_method': None}, events={'holidays_to_model_separately': 'auto', 'holiday_lookup_countries': 'auto', 'holiday_pre_num_days': 2, 'holiday_post_num_days': 2, 'holiday_pre_post_num_dict': None, 'daily_event_df_dict': None}, growth={'growth_term': 'linear'}, hyperparameter_override=None, regressors={'regressor_cols': []}, lagged_regressors={'lagged_regressor_dict': None}, seasonality={'yearly_seasonality': 'auto', 'quarterly_seasonality': 'auto', 'monthly_seasonality': 'auto', 'weekly_seasonality': 'auto', 'daily_seasonality': 'auto'}, uncertainty={'uncertainty_dict': None})¶: Defines the SILVERKITE template. Contains automatic growth, seasonality, holidays, and interactions. Does not include autoregression. Best for hourly and daily frequencies. Uses SimpleSilverkiteEstimator.

greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_WITH_AR = ModelComponentsParam(autoregression={'autoreg_dict': 'auto', 'simulation_num': 10}, changepoints={'changepoints_dict': None, 'seasonality_changepoints_dict': None}, custom={'fit_algorithm_dict': {'fit_algorithm': 'ridge', 'fit_algorithm_params': None}, 'feature_sets_enabled': 'auto', 'max_daily_seas_interaction_order': 5, 'max_weekly_seas_interaction_order': 2, 'extra_pred_cols': [], 'drop_pred_cols': None, 'explicit_pred_cols': None, 'min_admissible_value': None, 'max_admissible_value': None, 'regression_weight_col': None, 'normalize_method': None}, events={'holidays_to_model_separately': 'auto', 'holiday_lookup_countries': 'auto', 'holiday_pre_num_days': 2, 'holiday_post_num_days': 2, 'holiday_pre_post_num_dict': None, 'daily_event_df_dict': None}, growth={'growth_term': 'linear'}, hyperparameter_override=None, regressors={'regressor_cols': []}, lagged_regressors={'lagged_regressor_dict': None}, seasonality={'yearly_seasonality': 'auto', 'quarterly_seasonality': 'auto', 'monthly_seasonality': 'auto', 'weekly_seasonality': 'auto', 'daily_seasonality': 'auto'}, uncertainty={'uncertainty_dict': None})¶: Defines the SILVERKITE_WITH_AR template. Has the same config as SILVERKITE except for adding autoregression. Best for short-term daily forecasts. Uses SimpleSilverkiteEstimator.

greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_DAILY_1 = ['SILVERKITE_DAILY_1_CONFIG_1', 'SILVERKITE_DAILY_1_CONFIG_2', 'SILVERKITE_DAILY_1_CONFIG_3']¶: Defines the SILVERKITE_DAILY_1 template, which contains 3 candidate configs for grid search, optimized for the seasonality and changepoint parameters. Best for 1-day forecast for daily time series. Uses SimpleSilverkiteEstimator.

greykite.framework.templates.simple_silverkite_template_config.MULTI_TEMPLATES = {'SILVERKITE_DAILY_1': ['SILVERKITE_DAILY_1_CONFIG_1', 'SILVERKITE_DAILY_1_CONFIG_2', 'SILVERKITE_DAILY_1_CONFIG_3'], 'SILVERKITE_DAILY_90': ['DAILY_SEAS_LTQM_GR_LINEAR_CP_LT_HOL_SP2_FEASET_AUTO_ALGO_LINEAR_AR_OFF_DSI_AUTO_WSI_AUTO', 'DAILY_SEAS_LTQM_GR_LINEAR_CP_NONE_HOL_SP2_FEASET_AUTO_ALGO_LINEAR_AR_OFF_DSI_AUTO_WSI_AUTO', 'DAILY_SEAS_LTQM_GR_LINEAR_CP_LT_HOL_SP2_FEASET_AUTO_ALGO_RIDGE_AR_OFF_DSI_AUTO_WSI_AUTO', 'DAILY_SEAS_NM_GR_LINEAR_CP_LT_HOL_SP4_FEASET_AUTO_ALGO_RIDGE_AR_OFF_DSI_AUTO_WSI_AUTO'], 'SILVERKITE_HOURLY_1': ['HOURLY_SEAS_LT_GR_LINEAR_CP_NONE_HOL_TG_FEASET_AUTO_ALGO_LINEAR_AR_AUTO', 'HOURLY_SEAS_NM_GR_LINEAR_CP_LT_HOL_SP4_FEASET_AUTO_ALGO_LINEAR_AR_AUTO', 'HOURLY_SEAS_LT_GR_LINEAR_CP_NM_HOL_SP4_FEASET_OFF_ALGO_RIDGE_AR_AUTO', 'HOURLY_SEAS_NM_GR_LINEAR_CP_NM_HOL_SP1_FEASET_AUTO_ALGO_RIDGE_AR_AUTO'], 'SILVERKITE_HOURLY_168': ['HOURLY_SEAS_LT_GR_LINEAR_CP_LT_HOL_SP4_FEASET_AUTO_ALGO_RIDGE_AR_OFF', 'HOURLY_SEAS_LT_GR_LINEAR_CP_LT_HOL_SP2_FEASET_AUTO_ALGO_RIDGE_AR_OFF', 'HOURLY_SEAS_NM_GR_LINEAR_CP_NONE_HOL_SP4_FEASET_OFF_ALGO_LINEAR_AR_AUTO', 'HOURLY_SEAS_NM_GR_LINEAR_CP_NM_HOL_SP1_FEASET_AUTO_ALGO_RIDGE_AR_OFF'], 'SILVERKITE_HOURLY_24': ['HOURLY_SEAS_LT_GR_LINEAR_CP_NM_HOL_SP4_FEASET_AUTO_ALGO_RIDGE_AR_AUTO', 'HOURLY_SEAS_LT_GR_LINEAR_CP_NONE_HOL_SP4_FEASET_AUTO_ALGO_RIDGE_AR_AUTO', 'HOURLY_SEAS_NM_GR_LINEAR_CP_LT_HOL_SP1_FEASET_OFF_ALGO_LINEAR_AR_AUTO', 'HOURLY_SEAS_NM_GR_LINEAR_CP_NM_HOL_SP4_FEASET_AUTO_ALGO_RIDGE_AR_AUTO'], 'SILVERKITE_HOURLY_336': ['HOURLY_SEAS_LT_GR_LINEAR_CP_LT_HOL_SP2_FEASET_AUTO_ALGO_RIDGE_AR_OFF', 'HOURLY_SEAS_LT_GR_LINEAR_CP_LT_HOL_SP4_FEASET_AUTO_ALGO_RIDGE_AR_OFF', 'HOURLY_SEAS_NM_GR_LINEAR_CP_LT_HOL_SP1_FEASET_AUTO_ALGO_LINEAR_AR_OFF', 'HOURLY_SEAS_NM_GR_LINEAR_CP_NM_HOL_SP1_FEASET_AUTO_ALGO_LINEAR_AR_AUTO'], 'SILVERKITE_WEEKLY': ['WEEKLY_SEAS_NM_GR_LINEAR_CP_NONE_HOL_NONE_FEASET_OFF_ALGO_LINEAR_AR_OFF_DSI_AUTO_WSI_AUTO', 'WEEKLY_SEAS_NM_GR_LINEAR_CP_LT_HOL_NONE_FEASET_OFF_ALGO_LINEAR_AR_OFF_DSI_AUTO_WSI_AUTO', 'WEEKLY_SEAS_HV_GR_LINEAR_CP_NM_HOL_NONE_FEASET_OFF_ALGO_RIDGE_AR_OFF_DSI_AUTO_WSI_AUTO', 'WEEKLY_SEAS_HV_GR_LINEAR_CP_LT_HOL_NONE_FEASET_OFF_ALGO_RIDGE_AR_OFF_DSI_AUTO_WSI_AUTO']}¶

A dictionary of multi templates.

Keys are the available multi templates names (valid strings for config.model_template).
Values correspond to a list of ModelComponentsParam.

greykite.framework.templates.simple_silverkite_template_config.SINGLE_MODEL_TEMPLATE_TYPE¶

Types accepted by SimpleSilverkiteTemplate for config.model_template for a single template.

alias of Union[str, greykite.framework.templates.autogen.forecast_config.ModelComponentsParam, greykite.framework.templates.simple_silverkite_template_config.SimpleSilverkiteTemplateOptions]

class greykite.framework.templates.simple_silverkite_template_config.SimpleSilverkiteTemplateConstants(COMMON_MODELCOMPONENTPARAM_PARAMETERS: Dict = <factory>, MULTI_TEMPLATES: Dict = <factory>, SILVERKITE: Union[str, greykite.framework.templates.autogen.forecast_config.ModelComponentsParam, greykite.framework.templates.simple_silverkite_template_config.SimpleSilverkiteTemplateOptions] = ModelComponentsParam(autoregression={'autoreg_dict': None, 'simulation_num': 10}, changepoints={'changepoints_dict': None, 'seasonality_changepoints_dict': None}, custom={'fit_algorithm_dict': {'fit_algorithm': 'ridge', 'fit_algorithm_params': None}, 'feature_sets_enabled': 'auto', 'max_daily_seas_interaction_order': 5, 'max_weekly_seas_interaction_order': 2, 'extra_pred_cols': [], 'drop_pred_cols': None, 'explicit_pred_cols': None, 'min_admissible_value': None, 'max_admissible_value': None, 'regression_weight_col': None, 'normalize_method': None}, events={'holidays_to_model_separately': 'auto', 'holiday_lookup_countries': 'auto', 'holiday_pre_num_days': 2, 'holiday_post_num_days': 2, 'holiday_pre_post_num_dict': None, 'daily_event_df_dict': None}, growth={'growth_term': 'linear'}, hyperparameter_override=None, regressors={'regressor_cols': []}, lagged_regressors={'lagged_regressor_dict': None}, seasonality={'yearly_seasonality': 'auto', 'quarterly_seasonality': 'auto', 'monthly_seasonality': 'auto', 'weekly_seasonality': 'auto', 'daily_seasonality': 'auto'}, uncertainty={'uncertainty_dict': None}), SILVERKITE_WITH_AR: Union[str, greykite.framework.templates.autogen.forecast_config.ModelComponentsParam, greykite.framework.templates.simple_silverkite_template_config.SimpleSilverkiteTemplateOptions] = ModelComponentsParam(autoregression={'autoreg_dict': 'auto', 'simulation_num': 10}, changepoints={'changepoints_dict': None, 'seasonality_changepoints_dict': None}, custom={'fit_algorithm_dict': {'fit_algorithm': 'ridge', 'fit_algorithm_params': None}, 'feature_sets_enabled': 'auto', 'max_daily_seas_interaction_order': 5, 'max_weekly_seas_interaction_order': 2, 'extra_pred_cols': [], 'drop_pred_cols': None, 'explicit_pred_cols': None, 'min_admissible_value': None, 'max_admissible_value': None, 'regression_weight_col': None, 'normalize_method': None}, events={'holidays_to_model_separately': 'auto', 'holiday_lookup_countries': 'auto', 'holiday_pre_num_days': 2, 'holiday_post_num_days': 2, 'holiday_pre_post_num_dict': None, 'daily_event_df_dict': None}, growth={'growth_term': 'linear'}, hyperparameter_override=None, regressors={'regressor_cols': []}, lagged_regressors={'lagged_regressor_dict': None}, seasonality={'yearly_seasonality': 'auto', 'quarterly_seasonality': 'auto', 'monthly_seasonality': 'auto', 'weekly_seasonality': 'auto', 'daily_seasonality': 'auto'}, uncertainty={'uncertainty_dict': None}), SILVERKITE_DAILY_1_CONFIG_1: Union[str, greykite.framework.templates.autogen.forecast_config.ModelComponentsParam, greykite.framework.templates.simple_silverkite_template_config.SimpleSilverkiteTemplateOptions] = ModelComponentsParam(autoregression={'autoreg_dict': 'auto', 'simulation_num': 10}, changepoints={'changepoints_dict': {'method': 'auto', 'resample_freq': '7D', 'regularization_strength': 0.809, 'potential_changepoint_distance': '7D', 'no_changepoint_distance_from_end': '7D', 'yearly_seasonality_order': 8, 'yearly_seasonality_change_freq': None}, 'seasonality_changepoints_dict': None}, custom={'fit_algorithm_dict': {'fit_algorithm': 'ridge', 'fit_algorithm_params': None}, 'feature_sets_enabled': 'auto', 'max_daily_seas_interaction_order': 5, 'max_weekly_seas_interaction_order': 2, 'extra_pred_cols': [], 'drop_pred_cols': None, 'explicit_pred_cols': None, 'min_admissible_value': None, 'max_admissible_value': None, 'regression_weight_col': None, 'normalize_method': None}, events={'holidays_to_model_separately': ("New Year's Day", 'Chinese New Year', 'Christmas Day', 'Independence Day', 'Thanksgiving', 'Labor Day', 'Good Friday', 'Easter Monday [England, Wales, Northern Ireland]', 'Memorial Day', 'Veterans Day'), 'holiday_lookup_countries': ('UnitedStates', 'UnitedKingdom', 'India', 'France', 'China'), 'holiday_pre_num_days': 2, 'holiday_post_num_days': 2, 'holiday_pre_post_num_dict': None, 'daily_event_df_dict': None}, growth={'growth_term': 'linear'}, hyperparameter_override=None, regressors={'regressor_cols': []}, lagged_regressors={'lagged_regressor_dict': None}, seasonality={'yearly_seasonality': 8, 'quarterly_seasonality': 0, 'monthly_seasonality': 7, 'weekly_seasonality': 1, 'daily_seasonality': 0}, uncertainty={'uncertainty_dict': None}), SILVERKITE_DAILY_1_CONFIG_2: Union[str, greykite.framework.templates.autogen.forecast_config.ModelComponentsParam, greykite.framework.templates.simple_silverkite_template_config.SimpleSilverkiteTemplateOptions] = ModelComponentsParam(autoregression={'autoreg_dict': 'auto', 'simulation_num': 10}, changepoints={'changepoints_dict': {'method': 'auto', 'resample_freq': '7D', 'regularization_strength': 0.624, 'potential_changepoint_distance': '7D', 'no_changepoint_distance_from_end': '17D', 'yearly_seasonality_order': 1, 'yearly_seasonality_change_freq': None}, 'seasonality_changepoints_dict': None}, custom={'fit_algorithm_dict': {'fit_algorithm': 'ridge', 'fit_algorithm_params': None}, 'feature_sets_enabled': 'auto', 'max_daily_seas_interaction_order': 5, 'max_weekly_seas_interaction_order': 2, 'extra_pred_cols': [], 'drop_pred_cols': None, 'explicit_pred_cols': None, 'min_admissible_value': None, 'max_admissible_value': None, 'regression_weight_col': None, 'normalize_method': None}, events={'holidays_to_model_separately': ("New Year's Day", 'Chinese New Year', 'Christmas Day', 'Independence Day', 'Thanksgiving', 'Labor Day', 'Good Friday', 'Easter Monday [England, Wales, Northern Ireland]', 'Memorial Day', 'Veterans Day'), 'holiday_lookup_countries': ('UnitedStates', 'UnitedKingdom', 'India', 'France', 'China'), 'holiday_pre_num_days': 2, 'holiday_post_num_days': 2, 'holiday_pre_post_num_dict': None, 'daily_event_df_dict': None}, growth={'growth_term': 'linear'}, hyperparameter_override=None, regressors={'regressor_cols': []}, lagged_regressors={'lagged_regressor_dict': None}, seasonality={'yearly_seasonality': 1, 'quarterly_seasonality': 0, 'monthly_seasonality': 4, 'weekly_seasonality': 6, 'daily_seasonality': 0}, uncertainty={'uncertainty_dict': None}), SILVERKITE_DAILY_1_CONFIG_3: Union[str, greykite.framework.templates.autogen.forecast_config.ModelComponentsParam, greykite.framework.templates.simple_silverkite_template_config.SimpleSilverkiteTemplateOptions] = ModelComponentsParam(autoregression={'autoreg_dict': 'auto', 'simulation_num': 10}, changepoints={'changepoints_dict': {'method': 'auto', 'resample_freq': '7D', 'regularization_strength': 0.59, 'potential_changepoint_distance': '7D', 'no_changepoint_distance_from_end': '8D', 'yearly_seasonality_order': 40, 'yearly_seasonality_change_freq': None}, 'seasonality_changepoints_dict': None}, custom={'fit_algorithm_dict': {'fit_algorithm': 'ridge', 'fit_algorithm_params': None}, 'feature_sets_enabled': 'auto', 'max_daily_seas_interaction_order': 5, 'max_weekly_seas_interaction_order': 2, 'extra_pred_cols': [], 'drop_pred_cols': None, 'explicit_pred_cols': None, 'min_admissible_value': None, 'max_admissible_value': None, 'regression_weight_col': None, 'normalize_method': None}, events={'holidays_to_model_separately': ("New Year's Day", 'Chinese New Year', 'Christmas Day', 'Independence Day', 'Thanksgiving', 'Labor Day', 'Good Friday', 'Easter Monday [England, Wales, Northern Ireland]', 'Memorial Day', 'Veterans Day'), 'holiday_lookup_countries': ('UnitedStates', 'UnitedKingdom', 'India', 'France', 'China'), 'holiday_pre_num_days': 2, 'holiday_post_num_days': 2, 'holiday_pre_post_num_dict': None, 'daily_event_df_dict': None}, growth={'growth_term': 'linear'}, hyperparameter_override=None, regressors={'regressor_cols': []}, lagged_regressors={'lagged_regressor_dict': None}, seasonality={'yearly_seasonality': 40, 'quarterly_seasonality': 0, 'monthly_seasonality': 0, 'weekly_seasonality': 2, 'daily_seasonality': 0}, uncertainty={'uncertainty_dict': None}), SILVERKITE_COMPONENT_KEYWORDS: Type[enum.Enum] = <enum 'SILVERKITE_COMPONENT_KEYWORDS'>, SILVERKITE_EMPTY: Union[str, greykite.framework.templates.autogen.forecast_config.ModelComponentsParam, greykite.framework.templates.simple_silverkite_template_config.SimpleSilverkiteTemplateOptions] = 'DAILY_SEAS_NONE_GR_NONE_CP_NONE_HOL_NONE_FEASET_OFF_ALGO_LINEAR_AR_OFF_DSI_OFF_WSI_OFF', VALID_FREQ: List = <factory>, SimpleSilverkiteTemplateOptions: dataclasses.dataclass = <class 'greykite.framework.templates.simple_silverkite_template_config.SimpleSilverkiteTemplateOptions'>)[source]¶

Constants used by SimpleSilverkiteTemplate. Includes the model templates and their default values.

mutable_field is used when the default value is a mutable type like dict and list. Dataclass requires mutable default values to be wrapped in ‘default_factory’, so that instances of this dataclass cannot accidentally modify the default value. mutable_field wraps the constant accordingly.

COMMON_MODELCOMPONENTPARAM_PARAMETERS¶: Defines the default component values for SimpleSilverkiteTemplate. The components include seasonality, growth, holiday, trend changepoints, feature sets, autoregression, fit algorithm, etc. These are used when config.model_template provides the SimpleSilverkiteTemplateOptions.

MULTI_TEMPLATES¶

A dictionary of multi templates.

Keys are the available multi templates names (valid strings for config.model_template).
Values correspond to a list of ModelComponentsParam.

SILVERKITE¶: Defines the "SILVERKITE" template. Contains automatic growth, seasonality, holidays, and interactions. Does not include autoregression. Best for hourly and daily frequencies. Uses SimpleSilverkiteEstimator.

SILVERKITE_WITH_AR¶: Defines the SILVERKITE_WITH_AR template. Has the same config as SILVERKITE except for adding autoregression. Best for short-term daily forecasts. Uses SimpleSilverkiteEstimator.

SILVERKITE_DAILY_1_CONFIG_1¶: Config 1 in template SILVERKITE_DAILY_1. Compared to SILVERKITE, it adds change points and uses parameters specifically tuned for daily data and 1-day forecast.

SILVERKITE_DAILY_1_CONFIG_2¶: Config 2 in template SILVERKITE_DAILY_1. Compared to SILVERKITE, it adds change points and uses parameters specifically tuned for daily data and 1-day forecast.

SILVERKITE_DAILY_1_CONFIG_3¶: Config 3 in template SILVERKITE_DAILY_1. Compared to SILVERKITE, it adds change points and uses parameters specifically tuned for daily data and 1-day forecast.

class SILVERKITE_COMPONENT_KEYWORDS(value)¶: Valid values for simple silverkite template string name keywords. The names are the keywords and the values are the corresponding value enum. Can be used to create an instance of SimpleSilverkiteTemplateOptions.

SILVERKITE_EMPTY¶: Defines the "SILVERKITE_EMPTY" template. Everything here is None or off.

VALID_FREQ¶: Valid non-default values for simple silverkite template string name frequency. SimpleSilverkiteTemplateOptions.

class SimpleSilverkiteTemplateOptions(freq: greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_FREQ = <SILVERKITE_FREQ.DAILY: 'DAILY'>, seas: greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_SEAS = <SILVERKITE_SEAS.LT: 'LT'>, gr: greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_GR = <SILVERKITE_GR.LINEAR: 'LINEAR'>, cp: greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_CP = <SILVERKITE_CP.NONE: 'NONE'>, hol: greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_HOL = <SILVERKITE_HOL.NONE: 'NONE'>, feaset: greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_FEASET = <SILVERKITE_FEASET.OFF: 'OFF'>, algo: greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_ALGO = <SILVERKITE_ALGO.LINEAR: 'LINEAR'>, ar: greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_AR = <SILVERKITE_AR.OFF: 'OFF'>, dsi: greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_DSI = <SILVERKITE_DSI.AUTO: 'AUTO'>, wsi: greykite.framework.templates.simple_silverkite_template_config.SILVERKITE_WSI = <SILVERKITE_WSI.AUTO: 'AUTO'>)¶: Defines generic simple silverkite template options. Attributes can be set to different values using SILVERKITE_COMPONENT_KEYWORDS for high level tuning.

Changepoint Detection¶

class greykite.algo.changepoint.adalasso.changepoint_detector.ChangepointDetector[source]¶

A class to implement change point detection.

Currently supports long-term change point detection only. Input is a dataframe with time_col indicating the column of time info (the format should be able to be parsed by pd.to_datetime), and value_col indicating the column of observed time series values.

original_df¶

The original data df, used to retrieve original observations, if aggregation is used in fitting change points.

Type: pandas.DataFrame

time_col¶

The column name for time column.

Type: str

value_col¶

The column name for value column.

Type: str

trend_potential_changepoint_n¶

The number of change points that are evenly distributed over the time period.

Type: int

yearly_seasonality_order¶

The yearly seasonality order used when fitting trend.

Type: int

y¶

The observations after aggregation.

Type: pandas.Series

trend_df¶

The augmented df of the original_df, including regressors of trend change points and Fourier series for yearly seasonality.

Type: pandas.DataFrame

trend_model¶

The fitted trend model.

Type: sklearn.base.RegressionMixin

trend_coef¶

The estimated trend coefficients.

Type: numpy.array

trend_intercept¶

The estimated trend intercept.

Type: float

adaptive_lasso_coef¶

The list of length two, first element is estimated trend coefficients, and second element is intercept, both estimated by adaptive lasso.

Type: list

trend_changepoints¶

The list of detected trend change points, parsable by pd.to_datetime

Type: list

trend_estimation¶

The estimated trend with detected trend change points.

Type: pd.Series

seasonality_df¶

The augmented df of original_df, including regressors of seasonality change points with different Fourier series frequencies.

Type: pandas.DataFrame

seasonality_changepoints¶

The dictionary of detected seasonality change points for each component. Keys are component names, and values are list of change points.

Type: dict

seasonality_estimation¶

The estimated seasonality with detected seasonality change points. The series has the same length as original_df. Index is timestamp, and values are the estimated seasonality at each timestamp. The seasonality estimation is the estimated of seasonality effect with trend estimated by estimate_trend_with_detected_changepoints removed.

Type: pandas.Series

find_trend_changepoints : callable: Finds the potential trend change points for a given time series df.

plot : callable: Plot the results after implementing find_trend_changepoints.

find_trend_changepoints(df, time_col, value_col, yearly_seasonality_order=8, yearly_seasonality_change_freq=None, resample_freq='D', trend_estimator='ridge', adaptive_lasso_initial_estimator='ridge', regularization_strength=None, actual_changepoint_min_distance='30D', potential_changepoint_distance=None, potential_changepoint_n=100, no_changepoint_distance_from_begin=None, no_changepoint_proportion_from_begin=0.0, no_changepoint_distance_from_end=None, no_changepoint_proportion_from_end=0.0)[source]¶

Finds trend change points automatically by adaptive lasso.

The algorithm does an aggregation with a user-defined frequency, defaults daily.

If potential_changepoint_distance is not given, potential_changepoint_n potential change points are evenly distributed over the time period, else potential_changepoint_n is overridden by:

total_time_length / ``potential_changepoint_distance``

Users can specify either no_changepoint_proportion_from_end to specify what proportion from the end of data they do not want changepoints, or no_changepoint_distance_from_end (overrides no_changepoint_proportion_from_end) to specify how long from the end they do not want change points.

Then all potential change points will be selected by adaptive lasso, with the initial estimator specified by adaptive_lasso_initial_estimator. If user specifies regularization_strength, then the adaptive lasso will be run with a single tuning parameter calculated based on user provided prior, else a cross-validation will be run to automatically select the tuning parameter.

A yearly seasonality is also fitted at the same time, preventing trend from catching yearly periodical changes.

A rule-based guard function is applied at the end to ensure change points are not too close, as specified by actual_changepoint_min_distance.

Parameters

df (pandas.DataFrame) – The data df
time_col (str) – Time column name in df
value_col (str) – Value column name in df
yearly_seasonality_order (int, default 8) – Fourier series order to capture yearly seasonality.
yearly_seasonality_change_freq (DateOffset, Timedelta or str or None, default None) –
How often to change the yearly seasonality model. Set to None to disable this feature.

This is useful if you have more than 2.5 years of data and the detected trend without this feature is inaccurate because yearly seasonality changes over the training period. Modeling yearly seasonality separately over the each period can prevent trend changepoints from fitting changes in yearly seasonality. For example, if you have 2.5 years of data and yearly seasonality increases in magnitude after the first year, setting this parameter to “365D” will model each year’s yearly seasonality differently and capture both shapes. However, without this feature, both years will have the same yearly seasonality, roughly the average effect across the training set.

Note that if you use str as input, the maximal supported unit is day, i.e., you might use “200D” but not “12M” or “1Y”.
resample_freq (DateOffset, Timedelta or str, default “D”.) – The frequency to aggregate data. Coarser aggregation leads to fitting longer term trends.
trend_estimator (str in [“ridge”, “lasso” or “ols”], default “ridge”.) – The estimator to estimate trend. The estimated trend is only for plotting purposes. ‘ols’ is not recommended when yearly_seasonality_order is specified other than 0, because significant over-fitting will happen. In this case, the given value is overridden by “ridge”.
adaptive_lasso_initial_estimator (str in [“ridge”, “lasso” or “ols”], default “ridge”.) – The initial estimator to compute adaptive lasso weights
regularization_strength (float in [0, 1] or None) – The regularization for change points. Greater value implies fewer change points. 0 indicates all change points, and 1 indicates no change point. If None, the turning parameter will be selected by cross-validation. If a value is given, it will be used as the tuning parameter.
actual_changepoint_min_distance (DateOffset, Timedelta or str, default “30D”) – The minimal distance allowed between detected change points. If consecutive change points are within this minimal distance, the one with smaller absolute change coefficient will be dropped. Note: maximal unit is ‘D’, i.e., you may use units no more than ‘D’ such as ‘10D’, ‘5H’, ‘100T’, ‘200S’. The reason is that ‘W’, ‘M’ or higher has either cycles or indefinite number of days, thus is not parsable by pandas as timedelta.
potential_changepoint_distance (DateOffset, Timedelta, str or None, default None) – The distance between potential change points. If provided, will override the parameter potential_changepoint_n. Note: maximal unit is ‘D’, i.e., you may only use units no more than ‘D’ such as ‘10D’, ‘5H’, ‘100T’, ‘200S’. The reason is that ‘W’, ‘M’ or higher has either cycles or indefinite number of days, thus is not parsable by pandas as timedelta.
potential_changepoint_n (int, default 100) – Number of change points to be evenly distributed, recommended 1-2 per month, based on the training data length.
no_changepoint_distance_from_begin (DateOffset, Timedelta, str or None, default None) – The length of time from the beginning of training data, within which no change point will be placed. If provided, will override the parameter no_changepoint_proportion_from_begin. Note: maximal unit is ‘D’, i.e., you may only use units no more than ‘D’ such as ‘10D’, ‘5H’, ‘100T’, ‘200S’. The reason is that ‘W’, ‘M’ or higher has either cycles or indefinite number of days, thus is not parsable by pandas as timedelta.
no_changepoint_proportion_from_begin (float in [0, 1], default 0.0.) – potential_changepoint_n change points will be placed evenly over the whole training period, however, change points that are located within the first no_changepoint_proportion_from_begin proportion of training period will not be used for change point detection.
no_changepoint_distance_from_end (DateOffset, Timedelta, str or None, default None) – The length of time from the end of training data, within which no change point will be placed. If provided, will override the parameter no_changepoint_proportion_from_end. Note: maximal unit is ‘D’, i.e., you may only use units no more than ‘D’ such as ‘10D’, ‘5H’, ‘100T’, ‘200S’. The reason is that ‘W’, ‘M’ or higher has either cycles or indefinite number of days, thus is not parsable by pandas as timedelta.
no_changepoint_proportion_from_end (float in [0, 1], default 0.0.) – potential_changepoint_n change points will be placed evenly over the whole training period, however, change points that are located within the last no_changepoint_proportion_from_end proportion of training period will not be used for change point detection.

Returns

result – result dictionary with keys:

"trend_feature_df"pandas.DataFrame

The augmented df for change detection, in other words, the design matrix for the regression model. Columns:

’changepoint0’: regressor for change point 0, equals the continuous time of the observation minus the continuous time for time of origin.

…

’changepoint{potential_changepoint_n}’: regressor for change point {potential_changepoint_n}, equals the continuous time of the observation minus the continuous time of the {potential_changepoint_n}th change point.

’cos1_conti_year_yearly’: cosine yearly seasonality regressor of first order.

’sin1_conti_year_yearly’: sine yearly seasonality regressor of first order.

…

’cos{yearly_seasonality_order}_conti_year_yearly’ : cosine yearly seasonality regressor of {yearly_seasonality_order}th order.

’sin{yearly_seasonality_order}_conti_year_yearly’ : sine yearly seasonality regressor of {yearly_seasonality_order}th order.

"trend_changepoints"list

The list of detected change points.

"changepoints_dict"dict

The change point dictionary that is compatible as an input with forecast

"trend_estimation"pandas.Series

The estimated trend with detected trend change points.

Return type

dict

find_seasonality_changepoints(df, time_col, value_col, seasonality_components_df= name period order seas_names 0 tod 24.0 3 daily 1 tow 7.0 3 weekly 2 conti_year 1.0 5 yearly, resample_freq='H', regularization_strength=0.6, actual_changepoint_min_distance='30D', potential_changepoint_distance=None, potential_changepoint_n=50, no_changepoint_distance_from_end=None, no_changepoint_proportion_from_end=0.0, trend_changepoints=None)[source]¶

Finds the seasonality change points (defined as the time points where seasonality magnitude changes, i.e., the time series becomes “fatter” or “thinner”.)

Subtracts the estimated trend from the original time series first, then uses regression-based regularization methods to select important seasonality change points. Regressors are built from truncated Fourier series.

If you have run find_trend_changepoints before running find_seasonality_changepoints with the same df, the estimated trend will be automatically used for removing trend in find_seasonality_changepoints. Otherwise, find_trend_changepoints will be run automatically with the same parameters as you passed to find_seasonality_changepoints. If you do not want to use the same parameters, run find_trend_changepoints with your desired parameter before calling find_seasonality_changepoints.

The algorithm does an aggregation with a user-defined frequency, default hourly.

The regression features consists of potential_changepoint_n + 1 blocks of predictors. The first block consists of Fourier series according to seasonality_components_df, and other blocks are a copy of the first block truncated at the corresponding potential change point.

If potential_changepoint_distance is not given, potential_changepoint_n potential change points are evenly distributed over the time period, else potential_changepoint_n is overridden by:

total_time_length / ``potential_changepoint_distance``

Users can specify either no_changepoint_proportion_from_end to specify what proportion from the end of data they do not want changepoints, or no_changepoint_distance_from_end (overrides no_changepoint_proportion_from_end) to specify how long from the end they do not want change points.

Then all potential change points will be selected by adaptive lasso, with the initial estimator specified by adaptive_lasso_initial_estimator. The regularization strength is specified by regularization_strength, which lies between 0 and 1.

A rule-based guard function is applied at the end to ensure change points are not too close, as specified by actual_changepoint_min_distance.

Parameters

df (pandas.DataFrame) – The data df
time_col (str) – Time column name in df
value_col (str) – Value column name in df
seasonality_components_df (pandas.DataFrame) – The df to generate seasonality design matrix, which is compatible with seasonality_components_df in find_seasonality_changepoints
resample_freq (DateOffset, Timedelta or str, default “H”.) – The frequency to aggregate data. Coarser aggregation leads to fitting longer term trends.
regularization_strength (float in [0, 1] or None, default 0.6.) – The regularization for change points. Greater value implies fewer change points. 0 indicates all change points, and 1 indicates no change point. If None, the turning parameter will be selected by cross-validation. If a value is given, it will be used as the tuning parameter. Here “None” is not recommended, because seasonality change has different levels, and automatic selection by cross-validation may produce more change points than desired. Practically, 0.6 is a good choice for most cases. Tuning around 0.6 is recommended.
actual_changepoint_min_distance (DateOffset, Timedelta or str, default “30D”) – The minimal distance allowed between detected change points. If consecutive change points are within this minimal distance, the one with smaller absolute change coefficient will be dropped. Note: maximal unit is ‘D’, i.e., you may use units no more than ‘D’ such as ‘10D’, ‘5H’, ‘100T’, ‘200S’. The reason is that ‘W’, ‘M’ or higher has either cycles or indefinite number of days, thus is not parsable by pandas as timedelta.
potential_changepoint_distance (DateOffset, Timedelta, str or None, default None) – The distance between potential change points. If provided, will override the parameter potential_changepoint_n. Note: maximal unit is ‘D’, i.e., you may only use units no more than ‘D’ such as ‘10D’, ‘5H’, ‘100T’, ‘200S’. The reason is that ‘W’, ‘M’ or higher has either cycles or indefinite number of days, thus is not parsable by pandas as timedelta.
potential_changepoint_n (int, default 50) – Number of change points to be evenly distributed, recommended 1 per month, based on the training data length.
no_changepoint_distance_from_end (DateOffset, Timedelta, str or None, default None) – The length of time from the end of training data, within which no change point will be placed. If provided, will override the parameter no_changepoint_proportion_from_end. Note: maximal unit is ‘D’, i.e., you may only use units no more than ‘D’ such as ‘10D’, ‘5H’, ‘100T’, ‘200S’. The reason is that ‘W’, ‘M’ or higher has either cycles or indefinite number of days, thus is not parsable by pandas as timedelta.
no_changepoint_proportion_from_end (float in [0, 1], default 0.0.) – potential_changepoint_n change points will be placed evenly over the whole training period, however, only change points that are not located within the last no_changepoint_proportion_from_end proportion of training period will be used for change point detection.
trend_changepoints (list or None) – A list of user specified trend change points, used to estimated the trend to be removed from the time series before detecting seasonality change points. If provided, the algorithm will not check existence of detected trend change points or run find_trend_changepoints, but will use these change points directly for trend estimation.

Returns

result – result dictionary with keys:

"seasonality_feature_df"pandas.DataFrame

The augmented df for seasonality changepoint detection, in other words, the design matrix for the regression model. Columns:

”cos1_tod_daily”: cosine daily seasonality regressor of first order at change point 0.

”sin1_tod_daily”: sine daily seasonality regressor of first order at change point 0.

…

”cos1_conti_year_yearly”: cosine yearly seasonality regressor of first order at change point 0.

”sin1_conti_year_yearly”: sine yearly seasonality regressor of first order at change point 0.

…

”cos{daily_seasonality_order}_tod_daily_cp{potential_changepoint_n}” : cosine daily seasonality regressor of {yearly_seasonality_order}th order at change point {potential_changepoint_n}.

”sin{daily_seasonality_order}_tod_daily_cp{potential_changepoint_n}” : sine daily seasonality regressor of {yearly_seasonality_order}th order at change point {potential_changepoint_n}.

…

”cos{yearly_seasonality_order}_conti_year_yearly_cp{potential_changepoint_n}” : cosine yearly seasonality regressor of {yearly_seasonality_order}th order at change point {potential_changepoint_n}.

”sin{yearly_seasonality_order}_conti_year_yearly_cp{potential_changepoint_n}” : sine yearly seasonality regressor of {yearly_seasonality_order}th order at change point {potential_changepoint_n}.

"seasonality_changepoints"dict`[`list`[`datetime]]

The dictionary of detected seasonality change points for each component. Keys are component names, and values are list of change points.

"seasonality_estimation"pandas.Series

The estimated seasonality with detected seasonality change points.: The series has the same length as original_df. Index is timestamp, and values are the estimated seasonality at each timestamp. The seasonality estimation is the estimated of seasonality effect with trend estimated by estimate_trend_with_detected_changepoints removed.

"seasonality_components_dfpandas.DataFrame

The processed seasonality_components_df. Daily component row is removed if inferred frequency or aggregation frequency is at least one day.

Return type

dict

plot(observation=True, observation_original=True, trend_estimate=True, trend_change=True, yearly_seasonality_estimate=False, adaptive_lasso_estimate=False, seasonality_change=False, seasonality_change_by_component=True, seasonality_estimate=False, plot=True)[source]¶

Makes a plot to show the observations/estimations/change points.

In this function, component parameters specify if each component in the plot is included or not. These are bool variables. For those components that are set to True, their values will be replaced by the corresponding data. Other components values will be set to None. Then these variables will be fed into plot_change

Parameters

observation (bool) – Whether to include observation
observation_original (bool) – Set True to plot original observations, and False to plot aggregated observations. No effect is observation is False
trend_estimate (bool) – Set True to add trend estimation.
trend_change (bool) – Set True to add change points.
yearly_seasonality_estimate (bool) – Set True to add estimated yearly seasonality.
adaptive_lasso_estimate (bool) – Set True to add adaptive lasso estimated trend.
seasonality_change (bool) – Set True to add seasonality change points.
seasonality_change_by_component (bool) – If true, seasonality changes will be plotted separately for different components, else all will be in the same symbol. No effect if seasonality_change is False
seasonality_estimate (bool) – Set True to add estimated seasonality. The seasonality if plotted around trend, so the actual seasonality shown is trend estimation + seasonality estimation.
plot (bool, default True) – Set to True to display the plot, and set to False to return the plotly figure object.

Returns

None (if plot == True) – The function shows a plot.
fig (plotly.graph_objects.Figure) – The plot object.

Benchmarking¶

class greykite.framework.benchmark.benchmark_class.BenchmarkForecastConfig(df: pandas.core.frame.DataFrame, configs: Dict[str, greykite.framework.templates.autogen.forecast_config.ForecastConfig], tscv: greykite.sklearn.cross_validation.RollingTimeSeriesSplit, forecaster: greykite.framework.templates.forecaster.Forecaster = <greykite.framework.templates.forecaster.Forecaster object>)[source]¶

Class for benchmarking multiple ForecastConfig on a rolling window basis.

df¶

Timeseries data to forecast. Contains columns [time_col, value_col], and optional regressor columns. Regressor columns should include future values for prediction.

Type: pandas.DataFrame

configs¶

Dictionary of model configurations. A model configuration is a ForecastConfig. See ForecastConfig for details on valid ForecastConfig. Validity of the configs for benchmarking is checked via the validate method.

Type: Dict [str, ForecastConfig]

tscv¶

Cross-validation object that determines the rolling window evaluation. See RollingTimeSeriesSplit for details. The forecast_horizon and periods_between_train_test parameters of configs are matched against that of tscv. A ValueError is raised if there is a mismatch.

Type: RollingTimeSeriesSplit

forecaster¶

Forecaster used to create the forecasts.

Type: Forecaster

is_run¶

Indicator of whether the run method is executed. After executing run, this indicator is set to True. Some class methods like get_forecast requires is_run to be True to be executed.

Type: bool, default False

result¶

Stores the benchmarking results. Has the same keys as configs.

Type: dict

forecasts¶

Merged DataFrame of forecasts, upper and lower confidence interval for all input configs. Also stores train end date and forecast step for each prediction.

Type: pandas.DataFrame, default None

validate()[source]¶

Validates the inputs to the class for the method run.

Raises a ValueError if there is a mismatch between the following parameters of configs and tscv:

forecast_horizon

periods_between_train_test

Raises ValueError if all the configs do not have the same coverage parameter.

run()[source]¶

Runs every config and stores the output of the forecast_pipeline. This function runs only if the configs and tscv are jointly valid.

Returns: self
Return type: Returns self. Stores pipeline output of every config in self.result.

extract_forecasts()[source]¶

Extracts forecasts, upper and lower confidence interval for each individual config. This is saved as a pandas.DataFrame with the name rolling_forecast_df within the corresponding config of self.result. e.g. if config key is “silverkite”, then the forecasts are stored in self.result["silverkite"]["rolling_forecast_df"].

This method also constructs a merged DataFrame of forecasts, upper and lower confidence interval for all input configs.

plot_forecasts_by_step(forecast_step: int, config_names: List = None, xlabel: str = 'ts', ylabel: str = 'y', title: str = None, showlegend: bool = True)[source]¶

Returns a forecast_step ahead rolling forecast plot. The plot consists one line for each valid. config_names. If available, the corresponding actual values are also plotted.

For a more customizable plot, see plot_multivariate

Parameters

forecast_step (int) – Which forecast step to plot. A forecast step is an integer between 1 and the forecast horizon, inclusive, indicating the number of periods from train end date to the prediction date (# steps ahead).
config_names (list [str], default None) – Which config results to plot. A list of config names. If None, uses all the available config keys.
xlabel (str or None, default TIME_COL) – x-axis label.
ylabel (str or None, default VALUE_COL) – y-axis label.
title (str or None, default None) – Plot title. If None, default is based on forecast_step.
showlegend (bool, default True) – Whether to show the legend.

Returns

fig – Interactive plotly graph. Plots multiple column(s) in self.forecasts against TIME_COL.

See plot_forecast_vs_actual return value for how to plot the figure and add customization.

Return type

plotly.graph_objects.Figure

plot_forecasts_by_config(config_name: str, colors: List = ['rgb(31, 119, 180)', 'rgb(255, 127, 14)', 'rgb(44, 160, 44)', 'rgb(214, 39, 40)', 'rgb(148, 103, 189)', 'rgb(140, 86, 75)', 'rgb(227, 119, 194)', 'rgb(127, 127, 127)', 'rgb(188, 189, 34)', 'rgb(23, 190, 207)'], xlabel: str = 'ts', ylabel: