One of the main challenges of forecasting with the ARIMA family of models is the cumbersome tuning process of the models. As we saw in this chapter, this process includes many manual steps that are required for verifying the structure of the series (stationary or non-stationary), data transformations, descriptive analysis with the ACF and PACF plots to identify the type of process, and eventually tune the model parameters. While it might take a few minutes to train an ARIMA model for a single series, it may not scale up if you have dozens of series to forecast.
The auto.arima function from the forecast package provides a solution to this issue. This algorithm automates the tuning process of the ARIMA model with the use of statistical methods to identify both the structure of the series (stationary or not) and type (seasonal or not), and sets the model&apos...
