When evaluating the overall forecast performance of a model, the individual forecast errors are typically

Select one:

a. Summed over all errors first and this sum is squared to derive MSE (Mean Squared Error)

b. Summed over all errors first and then averaged for the last m periods where m is the seasonal periodicity of the data

c. Squared first and then an average of the total sum of squared errors is derived as the MSE (Mean Squared Error)

d. Divided by the respective value of the time series first, summed over all errors, then an average of this sum is the MAPE (Mean Absolute % Error)

e. Both c) and d) are correct