A note on a comparison of exponential smoothing methods for forecasting seasonal series

Abstract

Additive seasonal models and multiplicative seasonal models can be forecast using general exponential smoothing and Winters' methods. The two forecasting methods were compared using 47 of the 1001 time series which were used in the M-competition. Values of the optimal smoothing constants found when fitting the models are shown. Although Winters' models always resulted in a better squared error fit, these models gave a better forecast in only 55% of the series.