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Multistage RBF neural network ensemble learning for exchange rates forecasting

文献类型：期刊论文

作者Yu, Lean1,2; Lai, Kin Keung2; Wang, Shouyang1
刊名NEUROCOMPUTING
出版日期2008-10-01
卷号71期号:16-18页码:3295-3302
关键词RBF neural networks Ensemble learning Conditional generalized variance Exchange rates prediction
ISSN号0925-2312
DOI10.1016/j.neucom.2008.04.029
英文摘要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.
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000260066100029
出版者ELSEVIER SCIENCE BV
源URL[http://ir.amss.ac.cn/handle/2S8OKBNM/5403]  
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
推荐引用方式
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.

入库方式： OAI收割

来源：数学与系统科学研究院

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