A Fast Method for Change Point Detection from Large-scale Time Series Based on Haar Wavelet and Binary Search Tree (HWBST)

摘要：

　　Generally,Change Point (CP) detection is time-consuming,especially from large-scale time series.In this paper,a fast method of CP detection is proposed based on Haar Wavelet (HW) and Binary Search Tree (BST),named HWBST.In this method,by multi-level HW,a Binary Search Tree,termed BSTcD,is constructed from a diagnosed time series,and two binary search criteria are introduced to detect abrupt change from root to leaf nodes in BSTcD.Then,the sensitivity and accuracy of HWBST are analyzed and evaluated on the simulated and Electrocardiogram (ECG) time series.The results show that HWBST has better performance than HW,KS,and T statistic methods,in terms of computation time,error,accuracy etc.

关键词： CP detection ECG Large-Scale time series Haar Wavelet (HW) Binary Search Tree (BST)

作者: QI Jin-peng ZHANG Qing PU Fang QI Jie

作者单位: College of Information Science & Technology,Donghua University,Shanghai,P.R. China,201620;The Austra The Australia e-Health Research,CSIRO Computation Informatics(CCI),Brisbane,QLD 4029,Australia Informationization Office,Donghua University,Shanghai,P.R. China,201620 College of Information Science & Technology,Donghua University,Shanghai,P.R. China,201620

会议类型: 国际会议

会议名称: The 33th Chinese Control Conference第33届中国控制会议

会议地点: 南京

会议语种:英文

页码: 506-511

在线出版日期: 2014-07-28（万方平台首次上网日期，不代表论文的发表时间）