Improving the centered CUSUMS statistic for structural break detection in time series

Authors:
Kwok Pan Pang;Kai Ming Ting
Affiliations:
Gippsland School of Computing and Information Technology, Monash University, Victoria, Australia;Gippsland School of Computing and Information Technology, Monash University, Victoria, Australia
Venue:
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Year:
2004

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Abstract

Structural break is one of the important concerns in non-stationary time series prediction The cumulative sum of square (CUSUMS) statistic proposed by Brown et al (1975) has been developed as a general method for detecting a structural break To better utilize this method, this paper analyses the operating conditions of the centered version of CUSUMS using three variables: the percentage of variance change, the post-break data size and the pre-break data size In traditional approach of the centered CUSUMS, all available data are used for the break detection Our analysis reveals that one can improve the accuracy of the break detection by either reducing the post-break data size or increasing pre-break data size Based on our analysis, we propose a modified test statistic The evidence shows that the modified statistic significantly improves the chance of detecting the structural breaks.