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Please use this identifier to cite or link to this item: http://ir.ncue.edu.tw/ir/handle/987654321/15999

Title: Integrating Recurrent SOM with Wavelet-Based Kernel Partial Least Square Regressions for Financial Forecasting
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Authors: Huang, Shian-Chang;Wu, Tung-Kuang
Contributors: 資訊管理學系
Keywords: Kernel method;Recurrent Self-Organizing Map;Support vector machine;Wavelet analysis;Hybrid model
Date: 2010-08
Issue Date: 2013-04-22T07:38:39Z
Publisher: Elsevier
Abstract: 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-mean-squared forecasting errors are significantly reduced.
Relation: Expert Systems with Applications, 37(8): 5698-5705
Appears in Collections:[資訊管理學系所] 期刊論文

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