Time Series Forecasting in Python

10 Forecasting multiple time series

This chapter covers

• Examining the VAR model
• Exploring Granger causality to validate the use of the VAR model
• Forecasting multiple time series using the VAR model

In the last chapter, you saw how the SARIMAX model can be used to include the impact of exogenous variables on a time series. With the SARIMAX model, the relationship is unidirectional: we assume that the exogenous variable has an impact on the target only.

However, it is possible that two time series have a bidirectional relationship, meaning that time series t1 is a predictor of time series t2, and time series t2 is also a predictor for time series t1. In such a case, it would be useful to have a model that can take this bidirectional relationship ...