FORECASTING WITH EXPONENTIAL SMOOTHING: SOME GUIDELINES FOR MODEL SELECTION

Abstract

Abstract Despite the general acceptance of exponential smoothing, the choice of a specific smoothing model is often a difficult problem. Previous research involving smoothing‐model comparisons and the penalties for selection of the wrong model has been limited. This paper evaluates the performance of a representative group of smoothing models over a variety of conditions in 9,000 simulated time series. Forecast‐error results demonstrate that a major disadvantage of adaptive smoothing models is their tendency to generate unstable forecasts, even during periods when mean demand itself is stable. Several trend‐adjusted smoothing models are shown to be robust forecasters, whether the time series actually display a trend or not.