Further extensions of adaptive extended exponential smoothing and comparison with the M-Competition

This study presents and evaluates three new approaches to nonintervention, extrapolative (time series) forecasting. This is an extension of the adaptive extended exponential smoothing methodology (AEES) that allows the model additional smoothing constant adaptability to improve forecasting accuracy. The performance of the basic AEES method and two enhancements are first compared to five other time series techniques on a limited, validation data set and then compared to the 24 methods used in the M-Competition. Comparisons are made across all 111 M-Competition data sets and across the yearly, quarterly, and monthly components of the 111 data sets. When empirically tested across the 111 M-Competition data series, the heuristic AEES approach generally provided improved or comparable accuracy. This result was repeated with the yearly data series. Results for the quarterly and monthly data series were mixed. Discussion of these results within the marketing context of sales forecasting is provided. He has published in theJournal of the Academy of Marketing Science, Journal of Forecasting, Columbia Journal of World Business, Industrial Marketing Management, Journal of Business Logistics, International Journal of Physical Distribution and Logistics Management, Transportation and Logistics Review, Transportation Journal, Research in Marketing, and other journals. His research interests include marketing logistics, market forecasting, computer simulation of marketing systems, and international business to business marketing. His research has been published in theJournal of the Academy of Marketing Science, Journal of Business Logistics, International Journal of Physical Distribution and Logistics Management, Transportation Journal, Industrial Marketing Management, Journal of Marketing Education, andJournal of Current Issues and Research in Advertising.