Forecasting univariate time series with auto_arima

For this recipe, you will need to install pmdarima, a Python library that includes auto_arima – a tool designed to automate the optimization and fitting of ARIMA models. The auto_arima implementation in Python is inspired by the popular auto.arima from the forecast package in R.As you’ve seen in earlier recipes, determining the correct orders for the AR and MA components can be challenging. While techniques such as examining ACF and PACF plots are helpful, finding the optimal model often involves training multiple models – a process known as hyperparameter tuning, which can be quite labor-intensive. This is where auto_arima shines, as it simplifies the effort.Instead of the naive brute-force approach of conducting a grid search to try every parameter combination, auto_arima uses a more efficient approach to finding the optimal parameters. The auto_arima function offers three approaches to finding optimal parameters...