Grid Search for ARIMA Model

This diagram illustrates the process of performing a grid search for hyperparameter tuning of an ARIMA model. The data is divided into training, validation, and test sets. The ARIMA model is then trained on different combinations of P, D, and Q values. The model’s performance is evaluated using a rolling window approach on the validation set, and the combination of P, D, and Q values that results in the lowest mean absolute error (MAE) is selected as the best parameters.