Improving the accuracy of nonlinear combined forecasting using neural networks

✍ Scribed by Shan Ming Shi; Li Da Xu; Bao Liu

• Publisher: Elsevier Science
• Year: 1999
• Tongue: English
• Weight: 251 KB
• Volume: 16
• Category: Article
• ISSN: 0957-4174
• DOI: 10.1016/s0957-4174(98)00030-x

✦ Synopsis

The aim of the work presented in this paper is to propose artificial neural networks (ANN) as a tool for nonlinear combination of forecasts. In this study, the performance of the networks is evaluated by comparing them to three individual forecasting methods and two conventional linear combining methods. The outcome of the comparison proved that the prediction by the ANN method generally performs better than those by individual forecasting methods, as well as linear combining methods. The paper suggests that the ANN method can be used as an alternative to conventional linear combining methods to achieve greater forecasting accuracy. Meanwhile, ANNs also can be integrated with many other approaches including connectionist expert systems to improve the prediction quality further.