Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/25449
Title: Adaptive neural network model for time-series forecasting
Authors: Wong, WK 
Xia, M
Chu, WC 
Keywords: Adaptive metrics
Forecasting
Neural networks
Time-series
Issue Date: 2010
Publisher: Elsevier Science Bv
Source: European journal of operational research, 2010, v. 207, no. 2, p. 807-816 How to cite?
Journal: European Journal of Operational Research 
Abstract: In this study, a novel adaptive neural network (ADNN) with the adaptive metrics of inputs and a new mechanism for admixture of outputs is proposed for time-series prediction. The adaptive metrics of inputs can solve the problems of amplitude changing and trend determination, and avoid the over-fitting of networks. The new mechanism for admixture of outputs can adjust forecasting results by the relative error and make them more accurate. The proposed ADNN method can predict periodical time-series with a complicated structure. The experimental results show that the proposed model outperforms the auto-regression (AR), artificial neural network (ANN), and adaptive k-nearest neighbors (AKN) models. The ADNN model is proved to benefit from the merits of the ANN and the AKN through its' novel structure with high robustness particularly for both chaotic and real time-series predictions.
URI: http://hdl.handle.net/10397/25449
DOI: 10.1016/j.ejor.2010.05.022
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