Configure a Forecast

Use the class Forecaster to create a forecast. It has a method, run_forecast_config that accepts the input data (df) and forecast configuration (config).

from greykite.framework.templates.autogen.forecast_config import (
    ComputationParam, EvaluationMetricParam, EvaluationPeriodParam,
    ForecastConfig, MetadataParam, ModelComponentsParam)
from greykite.framework.templates.forecaster import Forecaster

# defines forecast configuration
config=ForecastConfig(
    model_template=model_template,                       # which model template to use
    forecast_horizon=forecast_horizon,                   # how many steps ahead to forecast
    coverage=coverage,                                   # intended coverage of the prediction bands
    metadata_param=MetadataParam(...),                   # input data description
    evaluation_metric_param=EvaluationMetricParam(...),  # what metric to evaluate
    evaluation_period_param=EvaluationPeriodParam(...),  # how to evaluate (train/test splits)
    model_components_param=ModelComponentsParam(...),    # model template tuning parameters
    computation_param=ComputationParam(...),             # parallelization
)

# creates forecast
forecaster = Forecaster()
result = forecaster.run_forecast_config(
    df=df,         # input data
    config=config  # forecast configuration
)

For basic usage, provide df and specify a config containing

• model_template,

• forecast_horizon,

• coverage,

• metadata_param.

The function will parse your input data according to metadata_param. The output contains a forecast for the specified forecast horizon and coverage using the model template’s defaults.

The other parameters can be used to tune the model and define evaluation criteria. See the sections below for explanations.

df

Required. Your input data. See Examine Input Data for details on data format.

config

Optional. The forecast configuration. An instance of ForecastConfig.

The following sections explain each optional attribute.

model_template

Optional. Name of the model template. A model template is a pre-packaged forecasting configuration. You can tune the template using the other parameters in the config.

Examples:

model_template = "SILVERKITE"  # the default
model_template = "PROPHET"

For information about templates and how to choose one, see Choose a Model.

forecast_horizon

Optional. Number of periods to forecast into the future. Must be > 0.

If not provided, default is determined from input data frequency.

Examples:

forecast_horizon = 30      # one month ahead, for daily data
forecast_horizon = 365*24  # one year ahead, for hourly data
forecast_horizon = 52      # one year ahead, for weekly data

coverage

Optional. Intended coverage of the prediction interval. Must be between 0.0 and 1.0.

Prediction intervals quantify the uncertainty of the forecast. They create a band that goes above/below the forecasted value, to provide an upper/lower prediction.

coverage specifies what % of points you want to fall within the bands. Larger coverage results in wider bands.

Examples:

coverage = None  # no prediction interval
coverage = 0.80  # 80% of actuals should fall within the prediction interval
coverage = 0.95  # 95% of actuals should fall within the prediction interval

metadata

Optional. Specifies properties of the input df. An instance of MetadataParam.

The attributes are:

time_col : str, default "ts"
    name of timestamp column in df

value_col : str, default "y"
    name of value column in df (containing values to forecast)

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#timeseries-offset-aliases
    for a list of frequency aliases.
    If None, inferred by pd.infer_freq.
    Provide this parameter if df has missing timepoints.

    Examples:
    "BH" business hour frequency
    "H" hourly frequency
    "B", business day frequency
    "D", calendar day frequency
    "W", weekly frequency

    "M", month end frequency
    "SM", semi-month end frequency (15th and end of month)
    "BM", business month end frequency
    "MS", month start frequency
    "SMS", semi-month start frequency (1st and 15th)
    "BMS", business month start frequency

    "Q", quarter end frequency
    "BQ", business quarter end frequency
    "QS", quarter start frequency
    "BQS", business quarter start frequency

    "A" or "Y" year end frequency
    "BA" or "BY" business year end frequency
    "AS" or "YS" year start frequency
    "BAS" or  "BYS" business year start frequency

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`.

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 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
    `~greykite.common.features.adjust_anomalous_data.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``.

Examples:

from greykite.framework.templates.autogen.forecast_config import MetadataParam

metadata = MetadataParam(
    time_col="ts",       # this is the default (TIME_COL constant)
    value_col="y",       # this is the default (VALUE_COL constant)
    freq=None,           # infer
    date_format=None,    # infer
    anomaly_info=None,   # no adjustments
)

metadata = MetadataParam(
    time_col="date",
    value_col="sessions",
    freq="W",
    date_format="%Y-%m-%d-%H",
    train_end_date=datetime.datetime(2020, 3, 1),
    anomaly_info=None,
)

anomaly_info

An anomaly is a deviation in the metric that is not expected to occur again in the future. anomaly_info can be used to adjust your input data if there are known anomalies. For example, you can choose to mask anomalies or correct the value to their hypothetical value, had the anomaly not occurred. This way, the forecast model will not project the anomalous pattern into the future. In most cases, you do not know the hypothetical value, so masking is sufficient.

You can correct anomalies in df using anomaly_info. For parameter details, see adjust_anomalous_data. For an example, see Examine Input Data.

Note

Measurement errors are different from anomalies.

• Measurement error: the actual value is misreported. Correct the values in df before calling run_forecast_config. For example, the database is corrupted, or a tracking error causes the actual value to be underreported.

• Anomaly: the measurements are accurate, but the typical pattern is disrupted in a one-time event. Correct these via anomaly_info. For example, a site issue causes the actual value to drop by 20% for 1 hour.

Tip

It’s important to provide freq if the input data has missing timepoints. pandas.infer_freq has trouble with missing values.

evaluation_metric

Optional. Defines the metrics used to evaluate the forecast. An instance of EvaluationMetricParam.

The attributes are:

cv_selection_metric : str or None, default "MeanAbsolutePercentError"
    EvaluationMetricEnum name, e.g. "MeanAbsolutePercentError"
    Used to select the optimal model during cross-validation.
    Defines ``score_func``, ``score_func_greater_is_better`` in ``forecast_pipeline``.

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 ``cv_selection_metric``.

        - If the string constant `greykite.common.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
          `greykite.common.evaluation.EvaluationMetricEnum` member names
          and `~greykite.common.constants.FRACTION_OUTSIDE_TOLERANCE`.

          For example::

            ["MeanSquaredError", "MeanAbsoluteError", "MeanAbsolutePercentError", "MedianAbsolutePercentError", "FractionOutsideTolerance2"]

        - If None, no additional metrics are computed.

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.

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
    `~greykite.common.evaluation.r2_null_model_score`.

    The null model is a `~sklearn.dummy.DummyRegressor`,
    which returns constant predictions.

    Valid keys are "strategy", "constant", "quantile".
    See https://scikit-learn.org/stable/modules/generated/sklearn.dummy.DummyRegressor.html

    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``.
    If `None`, the metric is not computed.

EvaluationMetricEnum names (valid for cv_selection_metric and cv_report_metrics) are listed below. See their descriptions at: EvaluationMetricEnum.

"MeanSquaredError"
"RootMeanSquaredError"
"MeanAbsoluteError"
"MedianAbsoluteError"
"MeanAbsolutePercentError"
"MedianAbsolutePercentError"
"SymmetricMeanAbsolutePercentError"
"Quantile80"  # quantile loss, 80th quantile
"Quantile95"  # quantile loss, 95th quantile
"Quantile99"  # quantile loss, 99th quantile

# auxiliary metrics (typically not optimized directly)
"CoefficientOfDetermination"  # also known as "R2", `1.0 - MeanSquaredError / variance(actuals)`
"FractionOutsideTolerance1"   # fraction of errors > 1%
"FractionOutsideTolerance2"   # fraction of errors > 2%
"FractionOutsideTolerance3"   # fraction of errors > 3%
"FractionOutsideTolerance4"   # fraction of errors > 4%
"FractionOutsideTolerance5"   # fraction of errors > 5%
"Correlation"                 # correlation between forecast and actuals

In most cases, use “MeanAbsolutePercentError” as the selection metric. Because it is a relative metric, it is comparable across forecasts.

See r2_null_model_score for the relationship between “CoefficientOfDetermination” (“R2”) and “R2_null_model_score”.

To assess model quality, “CoefficientOfDetermination” (“R2”) is preferred over “Correlation”. (They are equivalent for linear regression.) “CoefficientOfDetermination” accounts for bias whereas “Correlation” does not.

Examples:

from greykite.common.constants import CV_REPORT_METRICS_ALL
from greykite.common.evaluation import EvaluationMetricEnum
from greykite.framework.templates.autogen.forecast_config import EvaluationMetricParam

# Evaluates without aggregating.
# Calculates R2_null_model_score against null model that predicts 80th quantile.
# Note that the null model predicts the 0.8 quantile of the
#   training set, which matches `cv_selection_metric`.
# Reports all available metrics on each CV split.
# 5% tolerance level to compute "Outside Tolerance" metric.
evaluation_metric = EvaluationMetricParam(
    cv_selection_metric=EvaluationMetricEnum.Quantile80.name,
    cv_report_metrics=CV_REPORT_METRICS_ALL,  # the default, recommended
    agg_periods=None,
    agg_func=None,
    null_model_params = {
        "strategy": "quantile",
        "constant": None,
        "quantile": 0.8
    },
    relative_error_tolerance=0.05
)

# Creates forecast using daily data, evaluates accuracy of weekly totals.
# Null model predicts mean of training set.
# Reports a few extra metrics on each CV split.
# 1% tolerance level to compute "Outside Tolerance" metric.
evaluation_metric = EvaluationMetricParam(
    cv_selection_metric=EvaluationMetricEnum.MeanAbsolutePercentError.name,
    cv_report_metrics=[
        EvaluationMetricEnum.MeanSquaredError.name,
        EvaluationMetricEnum.MeanAbsoluteError.name,
        EvaluationMetricEnum.MedianAbsoluteError.name,
        EvaluationMetricEnum.MedianAbsolutePercentError.name,
    ],
    agg_periods=7,
    agg_func=np.sum,
    null_model_params = {
        "strategy": "mean"
    },
    relative_error_tolerance=0.01
)

Note

If you specify agg_periods, agg_func, we calculate all evaluation metrics after aggregation, but the forecast is returned at the same frequency as the input df.

Currently, these are 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.

evaluation_period

Optional. Defines how to split the data into train/test sets for evaluation. An instance of EvaluationPeriodParam.

Greykite runs the following steps for evaluation:

1. Run time-series cross validation (CV) to select the best hyperparameters, via grid search

2. Retrain and predict on holdout backtest period using best model

3. Retrain and predict on forecast period using best model

To do this, Greykite separates the data into three segments (training, backtest, forecast) as shown below. Each row corresponds to a train/test split. We record train and test error for each split (the average and std. are reported for CV).

x = train period
- = forecast period
  = not used

| TRAINING                     | BACKTEST    | FORECAST    |

xxxxxxxxxxxxx----                                              (cross-validation)
xxxxxxxxxxxxxxxxx----                                          (cross-validation)
xxxxxxxxxxxxxxxxxxxxx----                                      (cross-validation)
xxxxxxxxxxxxxxxxxxxxxxxxx----                                  (cross-validation)

xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx--------------                 (backtest)

xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx--------------   (forecast)

evaluation_period has these attributes:

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.
    Applies to both backtest and forecast, however the behaviour is slightly different.
    Check the illustration of test parameters for a visual explanation.
    If None, default is 0.

cv_horizon : int or None, default None
    Number of periods in each CV test set
    If None, default is forecast_horizon. Set 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 determined by template
    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`, optional, default False
    If True, splits from the end of the dataset are used.
    Else a sampling strategy is applied. Check
    `~greykite.sklearn.cross_validation.RollingTimeSeriesSplit._sample_splits` for details.

cv_periods_between_splits : int or None, default None
    Number of periods to slide the test window between CV splits. Has to be greater than or equal to 1.
    If None, default is cv_horizon.

cv_periods_between_train_test : int, default 0
    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.

To illustrate the test parameters:

(x) = train period
(-) = forecast period
(|) = train_end_date

backtest
(train_data)(periods_between_train_test)(test_horizon) |
xxxxxxxxxxxx                             ------------- |
                                                       |
forecast                                               |
(train_data)                                           | (periods_between_train_test)(forecast_horizon)
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx|                              -----------------

etc.

To illustrate the CV parameters:

(x) = train period
(-) = forecast period

SPLIT 1
(cv_min_train_periods)(cv_periods_between_train_test)(cv_horizon)
xxxxxxxxxxxxxxxxxxxxxx                               ------------

SPLIT 2: If cv_expanding_window = False
(cv_period_between_splits)(cv_min_train_periods)(cv_periods_between_train_test)(cv_horizon)
                          xxxxxxxxxxxxxxxxxxxxxx                               ------------

SPLIT 2: If cv_expanding_window = True
(cv_period_between_splits)(cv_min_train_periods)(cv_periods_between_train_test)(cv_horizon)
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx                               ------------

etc.

Note

The defaults are designed for proper evaluation based on your forecast_horizon and periods_between_train_test, by matching forecast_horizon=test_horizon=cv_horizon, and periods_between_train_test=cv_periods_between_train_test.

You can reduce the values if you don’t have sufficient data to evaluate.

Examples:

from greykite.framework.templates.autogen.forecast_config import EvaluationPeriodParam

# daily data, 3mo evaluation
evaluation_period = EvaluationPeriodParam(
    test_horizon=90,
    cv_horizon=90,
    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=0,
    cv_max_splits=3,
)

# Use CV to check 3 step-ahead error (cv_periods_between_train_test + cv_horizon)
evaluation_period = EvaluationPeriodParam(
    test_horizon=1,
    periods_between_train_test=2,
    cv_horizon=1,
    cv_min_train_periods=90,
    cv_expanding_window=True,
    cv_use_most_recent_splits=False,
    cv_periods_between_splits=1,
    cv_periods_between_train_test=2,
    cv_max_splits=None,
)

model_components

Optional. Tuning parameters for the selected model_template. An instance of ModelComponentsParam.

While the other parameters define input data and evaluation approach, these parameters allow you to tune the forecast model.

• On how to choose a template, see Choose a Model Template.

• For details about the model_components for each model template, see Greykite models and components.

computation

Optional. Parameters related to grid search computation. An instance of ComputationParam.

The attributes are:

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
        20 if any value is a distribution to sample from

n_jobs : int or None, default=-1
    Number of jobs to run in parallel during grid search
    ``None`` is treated as 1. ``-1`` uses all processors

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

Examples:

from greykite.framework.templates.autogen.forecast_config import ComputationParam

computation = ComputationParam(
    hyperparameter_budget=3,
    n_jobs=-1,
    verbose=1
)

# for error messages/debugging, do not
# run in parallel, and increase verbosity
computation = ComputationParam(
    hyperparameter_budget=None,
    n_jobs=1,
    verbose=2
)