How does the average variance calculated by mean percentage error relate historical actual values?

The average variance calculated by mean percentage error (MPE) provides insight into how the forecasted values deviate from historical actual values in a relative manner, as a percentage. This measure helps to assess the performance of the forecasting model by indicating the size of the error relative to the actual values.

By expressing errors as a percentage of the actual values, MPE allows for easy interpretation of the accuracy of forecasts, regardless of the scale of the data involved. This relative approach highlights not just the deviation, but also considers the context of the actual values, which is crucial in understanding forecast accuracy in real-world applications.

This is beneficial for identifying trends in forecasting performance and making necessary adjustments, ensuring that those involved in planning or forecasting can improve future predictions based on the errors observed from historical data. The percentage form allows comparisons across different periods or datasets that may have different magnitudes, therefore providing a standardized way to evaluate forecast accuracy.