Mean Square Error (MSE)

Mean square error (MSE) is one of the traditional measures of forecast accuracy. Mean square error is used when all of the errors are similar in magnitude. If the data does contain one or two large errors, calculate the mean absolute error (MAE), since using sum squares magnifies these errors. Also use the MAE or MSE to select the right forecasting model by choosing the model that results in the smallest MAE or MSE. Keep in mind that you cannot compare forecast models that used different data transformations and you cannot compare MSE to MAE.