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Multistage RBF neural network ensemble learning for exchange rates forecasting
Yu, Lean1,2; Lai, Kin Keung2; Wang, Shouyang1
2008-10-01
发表期刊NEUROCOMPUTING
ISSN0925-2312
卷号71期号:16-18页码:3295-3302
摘要In this study, a multistage nonlinear radial basis function (RBF) neural network ensemble forecasting model is proposed for foreign exchanger rates prediction. In the process of ensemble modeling, the first stage produces a great number of single RBF neural network models. In the second stage, a conditional generalized variance (CGV) minimization method is used to choose the appropriate ensemble members. In the final stage, another RBF network is used for neural network ensemble for prediction purpose. For testing purposes, we compare the new ensemble model's performance with some existing neural network ensemble approaches in terms of four exchange rates series. Experimental results reveal that the predictions using the proposed approach are consistently better than those obtained using the other methods presented in this study in terms of the same measurements. (C) 2008 Elsevier B.V. All rights reserved.
关键词RBF neural networks Ensemble learning Conditional generalized variance Exchange rates prediction
DOI10.1016/j.neucom.2008.04.029
语种英语
WOS研究方向Computer Science
WOS类目Computer Science, Artificial Intelligence
WOS记录号WOS:000260066100029
出版者ELSEVIER SCIENCE BV
引用统计
文献类型期刊论文
条目标识符http://ir.amss.ac.cn/handle/2S8OKBNM/5404
专题系统科学研究所
通讯作者Yu, Lean
作者单位1.Chinese Acad Sci, Acad Math & Syst Sci, Inst Syst Sci, Beijing 100190, Peoples R China
2.City Univ Hong Kong, Dept Management Sci, Kowloon, Hong Kong, Peoples R China
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GB/T 7714
Yu, Lean,Lai, Kin Keung,Wang, Shouyang. Multistage RBF neural network ensemble learning for exchange rates forecasting[J]. NEUROCOMPUTING,2008,71(16-18):3295-3302.
APA Yu, Lean,Lai, Kin Keung,&Wang, Shouyang.(2008).Multistage RBF neural network ensemble learning for exchange rates forecasting.NEUROCOMPUTING,71(16-18),3295-3302.
MLA Yu, Lean,et al."Multistage RBF neural network ensemble learning for exchange rates forecasting".NEUROCOMPUTING 71.16-18(2008):3295-3302.
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