Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 36, Issue 2, Pages 1529-1539Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2007.11.062
Keywords
Support vector regression; Self-organizing feature map; Feature selection; Prediction
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Stock market price index prediction is regarded as a challenging task of the financial time series prediction process. Support vector regression (SVR) has successfully solved prediction problems in many domains, including the stock market. This paper hybridizes SVR with the self-organizing feature map (SOFM) technique and a filter-based feature selection to reduce the cost of training time and to improve prediction accuracies. The hybrid system conducts the following processes: filter-based feature selection to choose important input attributes: SOFM algorithm to cluster the training samples; and SVR to predict the stock market price index. The proposed model was demonstrated using a real future dataset - Taiwan index futures (FITX) to predict the next day's price index. The experiment results show that the proposed SOFM-SVR is ail improvement over the traditional single SVR in average prediction accuracy and training time. (c) 2007 Elsevier Ltd. All rights reserved.
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