In this chapter, we introduced the ARIMA family of models, one of the core approaches for forecasting time series data. The main advantages of the ARIMA family of models is their flexibility and modularity, as they can handle both seasonal and non-seasonal time series data by adding or modifying the model components. In addition, we saw the applications of the ACF and PACF plots for identifying the type of process (for example, AR, MA, ARMA, and so on) and its order.
While it is essential to be familiar with the tuning process of ARIMA models, in practice, as the number series to be forecast increase, you may want to automate this process. The auto.arima function is one of the most common approaches in R to forecast with ARIMA models as it can scale up when dozens of series need to be forecast.
Last but not least, we saw applications of linear regression with the ARIMA...
