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Please use this identifier to cite or link to this item: http://ir.ncue.edu.tw/ir/handle/987654321/15996

Title: Integrating GA-Based Time-Scale Feature Extractions with SVMs for Stock Index Forecasting
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Authors: Huang, Shian-Chang;Wu, Tung-Kuang
Contributors: 資訊管理學系
Keywords: Hybrid forecasting;Support vector machine;Wavelet analysis;Genetic algorithm;Time series forecasting
Date: 2008-11
Issue Date: 2013-04-22T07:38:24Z
Publisher: Elsevier
Abstract: By integrating genetic algorithm (GA)-based optimal time-scale feature extractions with support vector machines (SVM), this study develops a novel hybrid prediction model that operates for multiple time-scale resolutions and utilizes a flexible nonparametric regressor to predict future evolutions of various stock indices. The time series of explanatory variables are decomposed using wavelet bases, and a GA is employed to extract optimal time-scale feature subsets from decomposed features. These extracted time-scale feature subsets then serve as an input for an SVM model that performs 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, 35(4): 2080-2088
Appears in Collections:[資訊管理學系所] 期刊論文

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