Using LS-SVM pattern recognizer to detect change-point in ARMA process

摘要：

　　Based on LS-SVM pattem recognizer,this paper develops an intelligent method for solving the problem of change-point detection,and the proposed model is applied to detect change-point of process mean-shift in auto-correlated time series process.In this research,LS-SVM algorithm and moving window method are used to detect the location of the mean shift signal,the LS-SVM pattern recognizer is designed and the performance of the recognizer is evaluated in terms of Accuracy Rate.Results of simulation experiment show that the proposed intelligent model is an effective method to detect change-point in ARMA data series.

关键词： change-points detection least squares support vector machine pattern recognition ARMA process

作者: Cheng Zhiqiang

作者单位: School of Management and Economics, North China University of Water Resources and Electric Power, Henan Zhengzhou, 450011, P.R.China.

会议类型: 国际会议

会议名称: 3rd International Conference on Frontiers of Manufacturing and Design Science(第三届制造与设计科学前沿国际会议(ICFMD))

会议地点: 香港

会议语种:英文

页码: 1731-1735

在线出版日期: 2012-12-11（万方平台首次上网日期，不代表论文的发表时间）