The sum of squared forecast errors divided by the number of observations is called:

The sum of squared forecast errors divided by the number of observations is known as the mean squared error (MSE). MSE is a key measure used to evaluate the accuracy of a forecast model. By squaring the errors (the differences between the predicted values and the actual values) before averaging them, MSE emphasizes larger errors more than smaller ones. This property makes MSE particularly useful in cases where larger discrepancies are more problematic. The division by the number of observations provides a normalized measure that allows for comparison across different datasets or forecasting models.

In contrast, the mean absolute error (MAE) calculates the average of the absolute values of the errors, treating all errors equally regardless of their size. The mean forecast deviation is less commonly used and typically refers to the average error without squaring, while mean variability error is not a standard term in forecasting or statistics, making it less relevant in this context.