An experiment on the modification procedures of seasonal factors in exponential smoothing models

Abstract

Adapting to changes over time is a primary point in any forecasting system. The quicker the adaptation the better the resulting forecasts. This is very important in computerized forecast-inventory control systems that manage thousands of parts. In such environments, it is difficult, if not impossible, to have all forecasts reviewed for possible changes in behaviour. This paper proposes two modifications to Winters' exponentially weighted moving-average model that provides improved forecasts for time series exhibiting seasonal behaviour. A set of tests is conducted to evaluate the performance of the original and modified Winters s models using 25 time series. The results indicate that average forecast errors have been reduced for the test series. The paper concludes with a discussion of the suitability of the proposed modifications under certain conditions.