Date of this Version
Previous research has shown that seasonal factors provide one of the most important ways to improve forecast accuracy. For example, in forecasts over an 18-month horizon for 68 monthly economic series from the M-Competition, Makridakis et al. (1984, Table 14) found that seasonal adjustments reduced the MAPE from 23.0 to 17.7 percent, an error reduction of 23%. On the other hand, research has also shown that seasonal factors sometimes increase forecast errors (e.g., Nelson, 1972).
So, when forecasting with a data series measured in intervals that represent part of a year, should one use seasonal factors or not? Statistical tests have been devised to answer this question, and they have been quite useful. However, some people might say that the question is not fair. Why does it have to be either/or? Shouldn’t the question be "to what extent should seasonal factors be used for a given series?"
Armstrong, J. S. (2004). Damped Seasonality Factors: Introduction. Retrieved from https://repository.upenn.edu/marketing_papers/53
Date Posted: 14 June 2007
This document has been peer reviewed.