The intuition behind the MSE metric to evaluate old forecasts is to sum up the forecast errors. to sum up the squared forecast errors. to sum up the absolute values of the forecast errors. to average the squared forecast errors. to average the absolute values of the forecast errors.

to average the squared forecast errors.

Explanation: Mean Squared Error MSE is used to measure accuracy and uses the average of squared errors. The errors are calculated by comparing the predicted figures with the actual numbers. The MSE is a positive value and when it produces a value close to zero, it indicates a better estimate quality than larger numbers.

MSE is calculated by subtracting the actual observation from the predicted value. This gives us the error. The errors are then squared, added up and their mean/average calculated.

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