The difference between the forecasted value (F) and the actual value (A). By convention, the error is generally reported as F minus A. Forecast errors serve three important functions: (1) The development of Mean Squared Error, which has been popular for years, should not be used for forecast comparisons because it is not independent of scale and it is unreliable compared to alternative measures. More appropriate measures include the APE (and the MdMAPE when summarizing across series) and the Relative Absolute Erros (and the MdRAE when summarizing across series). (3) Refining forecasting models, where the error measures should be sensitive to changes in the models being tested. Here, medians are less useful; the APE can be summarized by its mean (MAPE) and the RAE by its geometric mean (GmRAE). Armstrong and Collopy (1992a) provide empirical evidence to support these guidelines, and the measures are discussed in Armstrong (2001d).