(MSE). The sum of the squared forecast errors for each of the observations divided by the number of observations. It is an alternative to the Root Mean Square Error.) While MSE is popular among statisticians, it is unreliable and difficult to interpret. Armstrong and Fildes (1995) found no empirical support for the use of the MSE or RMSE in forecasting. Fortunately, better measures are available as discussed in Armstrong (2001d).
- Armstrong, J. S. & R. Fildes (1995), “On the
selection of error measures for comparisons among forecasting methods,”
*Journal of Forecasting*, 14, 67-71. (Full
text)
- Armstrong, J. S. (2001d), “Evaluating forecasting
methods,” in J. S. Armstrong (ed.),
*Principles of Forecasting.* Norwell,
MA: Kluwer Academic Press.