Detecting abrupt changes in a piecewise locally stationary time series

JOURNAL OF MULTIVARIATE ANALYSIS

卷:99

Article

Last, Michael¹  Shumway, Robert² 

[1] Natl Inst Stat Sci, Res Triangle Pk, NC 27709 USA

[2] Univ Calif Davis, Dept Stat, Davis, CA 95616 USA

关键词: Change-point;    Locally stationary;    Frequency domain;    Kullback-Leibler;   

DOI  :  10.1016/j.jmva.2007.06.010

来源: Elsevier

【 摘 要 】

Non-stationary time series arise in many settings, such as seismology, speech-processing, and finance. In many of these settings we are interested in points where a model of local stationarity is violated. We consider the problem of how to detect these change-points, which we identify by finding sharp changes in the time-varying power spectrum. Several different methods are considered, and we find that the symmetrized Kullback-Leibler information discrimination performs best in simulation studies. We derive asymptotic normality of our test statistic, and consistency of estimated change-point locations. We then demonstrate the technique on the problem of detecting arrival phases in earthquakes. (C) 2007 Elsevier Inc. All rights reserved.

【 授权许可】

Free