Forecasting Stock Index Trend based on the GA-SVM Integrated System and Wavelet-based Feature Extractions on Multiple Scales

摘要：

This paper proposes a novel GA-SVM integrated system for stock trend prediction based on wavelet-based feature extractions on multiple scales. The parameters of support vector machine (SVTV1) and kernel function are optimized by Genetic Algorithm (GA). Wavelet transformation is used to form the wavelet-scaling features. The Shanghai Stock Exchange (SSC) Composite index is selected for this study. Sufficient experiments are carried out, resulting in significant performances of the novel GA-SVM integrated system based on the wavelet-based feature selection method.

关键词： support vector machines genetic algorithm wavelet analysis integrated system prediction

作者: Sheng-Li Chen Yi-Jun Li Qiang Ye

作者单位: School of Management, Harbin Institute of Technology (HIT) Harbin, 150001, China

会议类型: 国际会议

会议名称: 2011 2nd IEEE International Conference on Emergency Management and Management Sciences(2011年第二届IEEE应急管理与管理科学国际会议 ICEMMS 2011)

会议地点: 北京

会议语种:英文

页码: 468-472

在线出版日期: 2011-08-08（万方平台首次上网日期，不代表论文的发表时间）