Multi-time series forecaster¶

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

No multivariate time series

A single model is trained, but each time series remains independent of the others. In other words, the past values of one series are not used as predictors of other series. Why is it useful then to model everything together? Although the series do not depend on each other, they may follow the same intrinsic pattern regarding their past and future values. For example, in the same store, the sales of products A and B may not be related, but they follow the same dynamics, that of the store.

In order to predict the next n steps, the same strategy of recursive multi-step forecasting is applied. The only difference is that, the series' name for which to estimate the predictions needs to be indicated.

Multivariate time series

All series are modeled in a single model considering that each time series depends not only on its past values but also on the past values of the other series. The forecaster is expected not only to learn the information of each series separately but also to relate them. For example, the measurements made by all the sensors (flow, temperature, pressure...) installed on an industrial machine such as a compressor.