Cookies

We use cookies to ensure that we give you the best experience on our website. By continuing to browse this repository, you give consent for essential cookies to be used. You can read more about our Privacy and Cookie Policy.


Durham Research Online
You are in:

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

Kotsialos, A. and Papageorgiou, M. and Poulimenos, A. (2005) 'Long-term sales forecasting using holt-winters and neural network methods.', Journal of forecasting., 24 (5). pp. 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.

Item Type:Article
Keywords:Feedforward neural networks.
Full text:Full text not available from this repository.
Publisher Web site:http://www3.interscience.wiley.com/cgi-bin/abstract/110574904/ABSTRACT
Record Created:26 Feb 2008
Last Modified:08 Apr 2009 16:21

Social bookmarking: del.icio.usConnoteaBibSonomyCiteULikeFacebookTwitterExport: EndNote, Zotero | BibTex
Usage statisticsLook up in GoogleScholar | Find in a UK Library