Multivariate Time Series

The models we discussed in the previous chapter only depended on the previous values of the single variable of interest. Those models are appropriate when we only have a single variable in our time series. However, it is common to have multiple variables in time-series data. Often, these other variables in the series can improve forecasting of the variable of interest. We will discuss models for time series with multiple variables in this chapter. We will first discuss the correlation relationship between time-series variables, then discuss how we can model multivariate time series. While there are many models for multivariate time-series data, we will discuss two models that are both powerful and widely used: autoregressive integrated moving average with exogenous variables (ARIMAX) and vector autoregressive (VAR). Understanding these two models will extend the reader’s model toolbox and provide building blocks for the reader to learn more about multivariate...