Error metrics are quantitative measures used to assess the accuracy of forecasting models by comparing predicted values against actual outcomes. They play a crucial role in evaluating forecast performance, enabling analysts to identify discrepancies and refine their models for improved accuracy. By using various error metrics, such as Mean Absolute Error (MAE) or Root Mean Square Error (RMSE), practitioners can better understand the reliability of their forecasts and make informed decisions based on the results.
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