Long-term sales forecasting using holt–winters and neural network methods
Article first published online: 2 AUG 2005
Copyright © 2005 John Wiley & Sons, Ltd.
Journal of Forecasting
Volume 24, Issue 5, pages 353–368, August 2005
How to Cite
Kotsialos, A., Papageorgiou, M. and Poulimenos, A. (2005), Long-term sales forecasting using holt–winters and neural network methods. J. Forecast., 24: 353–368. doi: 10.1002/for.943
- Issue published online: 2 AUG 2005
- Article first published online: 2 AUG 2005
- European Commission Programme ESPRIT
- long-term forecasting;
- Holt–Winters method;
- feedforward neural networks;
- sales forecasting
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.