This diagram illustrates the process of performing a grid search for hyperparameter tuning of an ARIMA model, along with the rolling window approach used for forecasting. The data is divided into training and validation sets. The ARIMA model is then trained on the training data and used to make forecasts for the validation data. The model’s performance is evaluated using a rolling window approach, where the training data is updated with each iteration.
Grid Search and Rolling Window for ARIMA Model
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