Calendar features for time series forecasting in AutoML

This article focuses on the calendar-based features that AutoML creates to increase the accuracy of forecasting regression models. Since holidays can have a strong influence on how the modeled system behaves, the time before, during, and after a holiday can bias the series' patterns. Each holiday generates a window over your existing dataset that the learner can assign an effect to. This can be especially useful in scenarios such as holidays that generate high demands for specific products. See the methods overview article for more general information about forecasting methodology in AutoML. Instructions and examples for training forecasting models in AutoML can be found in our set up AutoML for time series forecasting article.

As a part of feature engineering, AutoML transforms datetime type columns provided in the training data into new columns of calendar-based features. These features can help regression models learn seasonal patterns at several cadences. AutoML can always create calendar features from the time index of the time series since this is a required column in the training data. Calendar features are also made from other columns with datetime type, if any are present. See the how AutoML uses your data guide for more information on data requirements.

AutoML considers two categories of calendar features: standard features that are based entirely on date and time values and holiday features which are specific to a country or region of the world. We go over these features in the remainder of the article.

Standard calendar features

Th following table shows the full set of AutoML's standard calendar features along with an example output. The example uses the standard YY-mm-dd %H-%m-%d format for datetime representation.

The full set of standard calendar features may not be created in all cases. The generated set depends on the frequency of the time series and whether the training data contains datetime features in addition to the time index. The following table shows the features created for different column types:

Holiday features

AutoML can optionally create features representing holidays from a specific country or region. These features are configured in AutoML using the country_or_region_for_holidays parameter, which accepts an ISO country code.

The following table summarizes the holiday features:

AutoML uses Azure Open Datasets as a source for holiday information. For more information, see the PublicHolidays documentation.

To better understand the holiday feature generation, consider the following example data:

To make American holiday features for this data, we set the country_or_region_for_holiday to 'US' in the forecast settings as shown in the following code sample:

from azure.ai.ml import automl

# create a forcasting job
forecasting_job = automl.forecasting(
    compute='test_cluster',   # Name of single or multinode AML compute infrastructure created by user
    experiment_name=exp_name, # name of experiment 
    training_data=sample_data,  
    target_column_name='demand',
    primary_metric='NormalizedRootMeanSquaredError',
    n_cross_validations=3,
    enable_model_explainability=True
)

# set custom forecast settings
forecasting_job.set_forecast_settings(
    time_column_name='timeStamp',
    country_or_region_for_holidays='US'
)

The generated holiday features look like the following output:

Note that generated features have the prefix _automl_ prepended to their column names. AutoML generally uses this prefix to distinguish input features from engineered features.

Next steps

• Learn more about how to set up AutoML to train a time-series forecasting model.
• Browse AutoML Forecasting Frequently Asked Questions.
• Learn about AutoML Forecasting Lagged Features.
• Learn about how AutoML uses machine learning to build forecasting models.