Long-term sales forecasting using holt-winters and neural network methods

Markos Papageorgiou, Apostolos Kotsialos and Antonios Poulimenos
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Markos Papageorgiou: Technical University of Crete, Greece, Postal: Technical University of Crete, Greece
Apostolos Kotsialos: Technical University of Crete, Greece, Postal: Technical University of Crete, Greece
Antonios Poulimenos: Technical University of Crete, Greece, Postal: Technical University of Crete, Greece

Journal of Forecasting, 2005, vol. 24, issue 5, 353-368

Abstract: The problem of medium to long-term sales forecasting raises a number of requirements that must be suitably addressed in the design of the employed forecasting methods. These include long forecasting horizons (up to 52 periods ahead), a high number of quantities to be forecasted, which limits the possibility of human intervention, frequent introduction of new articles (for which no past sales are available for parameter calibration) and withdrawal of running articles. The problem has been tackled by use of a damped-trend Holt-Winters method as well as feedforward multilayer neural networks (FMNNs) applied to sales data from two German companies. Copyright © 2005 John Wiley & Sons, Ltd.

Date: 2005
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Persistent link: https://EconPapers.repec.org/RePEc:jof:jforec:v:24:y:2005:i:5:p:353-368

DOI: 10.1002/for.943

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