National Changhua University of Education Institutional Repository : Item 987654321/17081
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题名: Forecasting Stock Indices with Wavelet Domain Kernel Partial Least Square Regressions
作者: Huang, Shian-Chang
贡献者: 企業管理學系
关键词: Kernel method;Support vector machine;Wavelet analysis;Hybrid model;Time series forecasting
日期: 2011-12
上传时间: 2013-07-11T09:04:58Z
出版者: Elsevier Ltd.
摘要: Financial time series are nonlinear and non-stationary. Most financial phenomena cannot be clearly characterized in time domain. Therefore, traditional time domain models are not very effective in financial forecasting. To address the problem, this study combines wavelet analysis with kernel partial least square (PLS) regressions for stock index forecasting. Wavelet transformation maps time domain inputs to time-frequency (or wavelet) domain, where financial characteristics can be clearly identified. Because of the high dimensionality and heavy multi-collinearity of the input data, a wavelet domain kernel PLS regressor is employed to create the most efficient subspace that maintains maximum covariance between inputs and outputs, and to perform final forecasting. Empirical results demonstrate that the proposed model outperforms traditional neural networks, support vector machines, GARCH models, and has significantly reduced the forecasting errors.
關聯: Applied Soft Computing, 11(8): 5433-5443
显示于类别:[企業管理學系] 期刊論文

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