NDLI: A nonlinear combining forecast method based on fuzzy neural network

Content Provider	IEEE Xplore Digital Library
Author	Jing-Rong Dong
Copyright Year	2002
Description	Author affiliation: Dept. of Mathematic & Comput. Sci., Chongqing Normal Univ., China (Jing-Rong Dong)
Abstract	It has been shown in previous economic and statistical studies that combining forecasts may produce more accurate forecasts than individual ones. However, the literature on combining forecasts has almost exclusively focused on linear combined forecasts. The issues and methods of nonlinear combined forecasts have not yet been fully explored, even though forecast improvements may be possible using nonlinear combination techniques. We investigate the fuzzy neural network (FNN) as a tool for nonlinear combined forecasts. The performance of the networks is evaluated by comparing them to two individual forecasting methods and three conventional linear combining methods. The outcome of the comparison proved that the prediction by the FNN method generally performs better than those by individual forecasting methods, as well as linear combining methods. The paper suggests that the FNN method can be used as an alternative to conventional linear combining methods to achieve greater forecasting accuracy. Superiority of the FNN arises because of its flexibility in accounting for potentially complex nonlinear relationships not easily captured by traditional linear models.
Sponsorship	Hebei Univ. IEEE Syst., Man & Cybernetics Tech. Committe on Cybernetics
Starting Page	2160
Ending Page	2164
File Size	344600
Page Count	5
File Format	PDF
ISBN	0780375084
DOI	10.1109/ICMLC.2002.1175421
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-11-04
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy neural networks Economic forecasting Predictive models Artificial neural networks Fuzzy systems Fuzzy logic Mathematics Computer science Neural networks Testing
Content Type	Text
Resource Type	Article

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