National Changhua University of Education Institutional Repository : Item 987654321/17080
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Please use this identifier to cite or link to this item: http://ir.ncue.edu.tw/ir/handle/987654321/17080

Title: Integrating Spectral Clustering with Wavelet Based Kernel Partial Least Square Regressions for Financial Modeling and Forecasting
Authors: Huang, Shian-Chang
Contributors: 企業管理學系
Keywords: Wavelet analysis;Spectral clustering;Kernel method;Partial least squares;Financial forecasting
Date: 2011-02
Issue Date: 2013-07-11T09:04:58Z
Publisher: Elsevier Ltd.
Abstract: 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.
Relation: Applied Mathematics and Computation, 217(15): 6755-6764
Appears in Collections:[Department of Business Administration] Periodical Articles

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