Note

Click here to download the full example code

Simple Forecast

You can create and evaluate a forecast with just a few lines of code.

Provide your timeseries as a pandas dataframe with timestamp and value.

For example, to forecast daily sessions data, your dataframe could look like this:

import pandas as pd
df = pd.DataFrame({
    "date": ["2020-01-08-00", "2020-01-09-00", "2020-01-10-00"],
    "sessions": [10231.0, 12309.0, 12104.0]
})

The time column can be any format recognized by pandas.to_datetime.

In this example, we’ll load a dataset representing log(daily page views) on the Wikipedia page for Peyton Manning. It contains values from 2007-12-10 to 2016-01-20. More dataset info here.

27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
 from collections import defaultdict
 import warnings

 warnings.filterwarnings("ignore")

 import pandas as pd
 import plotly

 from greykite.common.data_loader import DataLoader
 from greykite.framework.templates.autogen.forecast_config import ForecastConfig
 from greykite.framework.templates.autogen.forecast_config import MetadataParam
 from greykite.framework.templates.forecaster import Forecaster
 from greykite.framework.templates.model_templates import ModelTemplateEnum
 from greykite.framework.utils.result_summary import summarize_grid_search_results

 # Loads dataset into pandas DataFrame
 dl = DataLoader()
 df = dl.load_peyton_manning()

 # specify dataset information
 metadata = MetadataParam(
     time_col="ts",  # name of the time column ("date" in example above)
     value_col="y",  # name of the value column ("sessions" in example above)
     freq="D"  # "H" for hourly, "D" for daily, "W" for weekly, etc.
               # Any format accepted by `pandas.date_range`
 )

Create a forecast

You can pick the PROPHET or SILVERKITE forecasting model template. (see Choose a Model).

In this example, we use SILVERKITE. You may also use PROPHET to see how a third-party library is leveraged in the same framework.

63
64
65
66
67
68
69
70
71
72
 forecaster = Forecaster()  # Creates forecasts and stores the result
 result = forecaster.run_forecast_config(  # result is also stored as `forecaster.forecast_result`.
     df=df,
     config=ForecastConfig(
         model_template=ModelTemplateEnum.SILVERKITE.name,
         forecast_horizon=365,  # forecasts 365 steps ahead
         coverage=0.95,         # 95% prediction intervals
         metadata_param=metadata
     )
 )

Out:

Fitting 3 folds for each of 1 candidates, totalling 3 fits

Check results

The output of run_forecast_config is a dictionary that contains the future forecast, historical forecast performance, and the original timeseries.

Timeseries

Let’s plot the original timeseries. run_forecast_config returns this as ts.

(The interactive plot is generated by plotly: click to zoom!)

88
89
90
 ts = result.timeseries
 fig = ts.plot()
 plotly.io.show(fig)

Cross-validation

By default, run_forecast_config provides historical evaluation, so you can see how the forecast performs on past data. This is stored in grid_search (cross-validation splits) and backtest (holdout test set).

Let’s check the cross-validation results. By default, all metrics in ElementwiseEvaluationMetricEnum are computed on each CV train/test split. The configuration of CV evaluation metrics can be found at Evaluation Metric. Below, we show the Mean Absolute Percentage Error (MAPE) across splits (see summarize_grid_search_results to control what to show and for details on the output columns).

108
109
110
111
112
113
114
115
116
117
118
 grid_search = result.grid_search
 cv_results = summarize_grid_search_results(
     grid_search=grid_search,
     decimals=2,
     # The below saves space in the printed output. Remove to show all available metrics and columns.
     cv_report_metrics=None,
     column_order=["rank", "mean_test", "split_test", "mean_train", "split_train", "mean_fit_time", "mean_score_time", "params"])
 # Transposes to save space in the printed output
 cv_results["params"] = cv_results["params"].astype(str)
 cv_results.set_index("params", drop=True, inplace=True)
 cv_results.transpose()
params []
rank_test_MAPE 1
mean_test_MAPE 7.31
split_test_MAPE (5.02, 8.53, 8.39)
mean_train_MAPE 4.2
split_train_MAPE (3.82, 4.25, 4.54)
mean_fit_time 6.32
mean_score_time 0.76


Backtest

Let’s plot the historical forecast on the holdout test set. You can zoom in to see how it performed in any given period.

125
126
127
 backtest = result.backtest
 fig = backtest.plot()
 plotly.io.show(fig)