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题名: Integrating Recurrent SOM with Wavelet-based Kernel Partial Least Square Regressions for Financial Forecasting
作者: Huang, Shian-Chang;Wu, Tung-Kuang
贡献者: 企業管理學系
关键词: Kernel method;Recurrent Self-Organizing Map;Support vector machine;Wavelet analysis;Hybrid model
日期: 2010-08
上传时间: 2013-07-11T09:04:43Z
出版者: Elsevier Ltd.
摘要: This study implements a novel expert system for financial forecasting. In the first stage, wavelet analysis transforms the input space of raw data to a time-scale feature space suitable for financial forecasting, and then a Recurrent Self-Organizing Map (RSOM) algorithm is used for partitioning and storing temporal context of the feature space. In the second stage, multiple kernel partial least square regressors (as local models) that best fit partitioned regions are constructed for final forecasting. Compared with neural networks, pure SVMs or traditional GARCH models, the proposed model performs best. The root-meansquared forecasting errors are significantly reduced.
關聯: Expert Systems with Applications, 37(8): 5698-5705
显示于类别:[企業管理學系] 期刊論文


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