Forecasting time series data using auto_arima

For this recipe, you must install pmdarima, a Python library that includes auto_arima for automating ARIMA hyperparameter optimization and model fitting. The auto_arima implementation in Python is inspired by the popular auto.arima from the forecast package in R.

In Chapter 10, Building Univariate Time Series Models Using Statistical Methods, you learned that finding the proper orders for the AR and MA components is not simple. Although you explored useful techniques for estimating the orders, such as interpreting the partial autocorrelation function (PACF) and autocorrelation function (ACF) plots, you may still need to train different models to find the optimal configurations (referred to as hyperparameter tuning). This can be a time-consuming process and is where auto_arima shines.

Instead of the naive approach of training multiple models through grid search to cover every possible combination of parameter values, auto_arima automates...