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Long‐term sales forecasting using holt–winters and neural network methods
Author(s) -
Kotsialos Apostolos,
Papageorgiou Markos,
Poulimenos Antonios
Publication year - 2005
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.943
Subject(s) - sales forecasting , artificial neural network , term (time) , computer science , calibration , german , econometrics , feedforward neural network , operations research , artificial intelligence , economics , statistics , mathematics , quantum mechanics , history , physics , archaeology
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.