What does the mean squared errors measure calculate?

The mean squared errors measure calculates the average of the residual sum of squares for the forecasts. This metric is fundamental in assessing the accuracy of a predictive model. By computing the average of the squared differences between the forecasted values and the actual values, it provides a comprehensive indication of how close the predictions are to the real outcomes.

Utilizing the squared differences ensures that any errors in prediction are treated positively, emphasizing larger discrepancies due to the squaring effect. Thus, it penalizes larger errors more heavily, which helps in optimizing the forecasting model. This averaging of squared errors results in a single value that reflects the overall performance of the model, enabling effective comparisons across different forecasting methods or parameter settings.