Using Related Time Series Datasets

Historical time series: time series without data points within the forecast horizon.

Forward-looking time series: time series with data points within the forecast horizon.

It can't include the target value from the target time series.

It must include item_id and timestamp dimensions, and at least one related feature (such as price).

Related time series feature data must be of the int or float datatypes.

In order to use the entire target time series, all items from the target time series dataset must also be included in the related time series dataset. If a related time series only contains a subset of items from the target time series, then the model creation and forecast generation will be limited to that specific subset of items.

For example, if the target time series contains 1000 items and the related time series dataset only contains 100 items, then the model and forecasts will be based on only those 100 items.

The frequency at which data is recorded in the related time series dataset must match the interval at which you want to generate forecasts (the forecasting granularity).

For example, if you want to generate forecasts at a weekly granularity, the frequency at which data is recorded in the related time series must also be weekly, even if the frequency at which data is recorded in the target time series is daily.

The data for each item in the related time series dataset must start on or before the beginning timestamp of the corresponding item_id in the target time series dataset.

For example, if the target time series data for socks starts at 2019-01-01 and the target time series data for shoes starts at 2019-02-01, the related time series data for socks must begin on or before 2019-01-01 and the data for shoes must begin on or before 2019-02-01.

For forward-looking related time series datasets, the last timestamp for every item must be on the last timestamp in the user-designated forecast window (called the forecast horizon).

In the example related time series file below, the timestamp data for both socks and shoes must end on or after 2019-07-01 (the last recorded timestamp) plus the forecast horizon. If data frequency in the target time series is daily and the forecast horizon is 10 days, daily data points must be provided in the forward-looking related time series file until 2019-07-11.

For historical related time series datasets, the last timestamp for every item must match the last timestamp in the target time series.

In the example related time series file below, the timestamp data for both socks and shoes must end on 2019-07-01 (the last recorded timestamp).

The Forecast dimensions provided in the related time series dataset must be either equal to or a subset of the dimensions designated in the target time series dataset.

Related time series cannot have missing values. For information on missing values in a related time series dataset, see Handling Missing Values.

The last data point was recorded in the target time series dataset on 2019-07-01.

The forecast horizon is 10 days.

The forecast granularity is daily (D).