期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 38, 期 5, 页码 5569-5576出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2010.10.079
关键词
Fractal feature selection; Support vector machine; Stock trend prediction
类别
资金
- National Nature Science Foundation of China [70871033]
- National High Technology Research and Development Program of China [2007AA04Z116]
- Science Research and Development Foundation of HeFei University of Technology [2009HGXJ0040]
Stock trend prediction is regarded as a challenging task. Recently many researches have shown that a successful feature selection method can improve the prediction accuracy of stock market. This paper hybridizes fractal feature selection method and support vector machine to predict the direction of daily stock price index. Fractal feature selection method is suitable for solving the nonlinear problem and it can exactly spot how many important features we should choose. To evaluate the prediction accuracy of this method, this paper compares its performance with other five commonly used feature selection methods. The results show fractal feature selection method selects the relatively smaller number of features and it achieves the best average prediction accuracy. (C) 2010 Elsevier Ltd. All rights reserved.
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