Model tuning is the process of adjusting the parameters and settings of a forecasting model to improve its accuracy and performance. This involves selecting the best model structure, optimizing hyperparameters, and evaluating model predictions against actual outcomes to ensure that the forecasts are as reliable as possible. Effective model tuning is crucial for generating point forecasts and establishing prediction intervals that reflect uncertainty in a given dataset.
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