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題名: Integrating Spectral Clustering with Wavelet Based Kernel Partial Least Square Regressions for Financial Modeling and Forecasting
作者: Huang, Shian-Chang
貢獻者: 企業管理學系
關鍵詞: Wavelet analysis;Spectral clustering;Kernel method;Partial least squares;Financial forecasting
日期: 2011-02
上傳時間: 2013-07-11T09:04:58Z
出版者: Elsevier Ltd.
摘要: Traditional forecasting models are not very effective in most financial time series. To address the problem, this study proposes a novel system for financial modeling and forecasting. In the first stage, wavelet analysis transforms the input space of raw data to a time-scale feature space suitable for financial modeling and forecasting. A spectral clustering algorithm is then used to partition the feature space into several disjointed regions according to their time series dynamics. In the second stage, multiple kernel partial least square regressors ideally suited to each partitioned region are constructed for final forecasting. The proposed model outperforms neural networks, SVMs, and traditional GARCH models, significantly reducing root-mean-squared forecasting errors.
關聯: Applied Mathematics and Computation, 217(15): 6755-6764
顯示於類別:[企業管理學系] 期刊論文


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