Independent multi-series forecasting¶

In univariate time series forecasting, a single time series is modeled as a linear or nonlinear combination of its lags, where past values of the series are used to forecast its future. In multi-series forecasting, two or more time series are modeled together using a single model.

In independent multi-series forecasting a single model is trained for all time series, but each time series remains independent of the others, meaning that past values of one series are not used as predictors of other series. However, modeling them together is useful because the series may follow the same intrinsic pattern regarding their past and future values. For instance, the sales of products A and B in the same store may not be related, but they follow the same dynamics, that of the store.

Internal Forecaster transformation of two time series and an exogenous variable into the matrices needed to train a machine learning model in a multi-series context.

To predict the next n steps, the strategy of recursive multi-step forecasting is applied, with the only difference being that the series name for which to estimate the predictions needs to be indicated.

Diagram of recursive forecasting with multiple independent time series.

Using the ForecasterAutoregMultiSeries and ForecasterAutoregMultiSeriesCustom classes, it is possible to easily build machine learning models for independent multi-series forecasting.