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 into account and output predictions for bothtime series simultaneously.
This brings us to the vector autoregression (VAR) model. This particular model allows us to capture the relationship between multiple time series as they change over time. That, in turn, allows us to produce forecasts for many time series simultaneously, therefore performing multivariate forecasting.