An intelligent model selection and forecasting system

✍ Scribed by A. R. Venkatachalam; Jeffrey E. Sohl

• Publisher: John Wiley and Sons
• Year: 1999
• Tongue: English
• Weight: 166 KB
• Volume: 18
• Category: Article
• ISSN: 0277-6693
• DOI: 10.1002/(sici)1099-131x(199905)18:3<167::aid-for715>3.0.co;2-f

✦ Synopsis

In this paper we present an intelligent decision-support system based on neural network technology for model selection and forecasting. While most of the literature on the application of neural networks in forecasting addresses the use of neural network technology as an alternative forecasting tool, limited research has focused on its use for selection of forecasting methods based on time-series characteristics. In this research, a neural network-based decision support system is presented as a method for forecast model selection. The neural network approach provides a framework for directly incorporating time-series characteristics into the modelselection phase. Using a neural network, a forecasting group is initially selected for a given data set, based on a set of time-series characteristics. Then, using an additional neural network, a speci®c forecasting method is selected from a pool of three candidate methods. The results of training and testing of the networks are presented along with conclusions.