SVM APPLICATION OF FINANCIAL TIME SERIES FORECASTING USING EMPIRICAL TECHNICAL INDICATORS

摘要：

Support vector machines (SVMs) are promising methods of pattern recognition in financial time series because they use a risk function consisting of the empirical error and a regularized term which is derived from the structural risk minimization principle. This study applies SVM to pattern recognition in the financial engineering domain. Compared with present machine learning methods in financial forecasting, this study does not simply work on the original time data series, but some interesting and empirical technical indicators. The experimental results show that transforming the input data space of SVM can bring good performance in finance engineering.

关键词： Support Vector Machines Financial time series

作者: Jianxue Chen

作者单位: College of Electrical and Electronic Engineering Shanghai University of Engineering Science Shanghai,P.R.China

会议类型: 国际会议

会议名称: 2010 International Conference on Information,Networking and Automation(2010 IEEE信息网络与自动化国际会议 ICINA 2010)

会议地点: 昆明

会议语种:英文

页码: 77-81

在线出版日期: 2010-10-17（万方平台首次上网日期，不代表论文的发表时间）