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Multiple change-point detection for non-stationary time series using wild binary segmentation

Korkas, Karolos K. and Fryzlewicz, Piotr (2017) Multiple change-point detection for non-stationary time series using wild binary segmentation. Statistica Sinica, 27 (1). pp. 287-311. ISSN 1017-0405

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Identification Number: 10.5705/ss.202015.0262

Abstract

We propose a new technique for consistent estimation of the number and locations of the change-points in the second-order structure of a time series. The core of the segmentation procedure is the Wild Binary Segmentation method(WBS), a technique which involves a certain randomised mechanism. The advantage of WBS over the standard Binary Segmentation lies in its localisation feature, thanks to which it works in cases where the spacings between change-points are short. In addition, we do not restrict the total number of change-points a time series can have. We also ameliorate the performance of our method by combining the CUSUM statistics obtained at different scales of the wavelet periodogram, our main change-point detection statistic, which allows a rigorous estimation of the local autocovariance of a piecewise-stationary process. We provide a simulation study to examine the performance of our method for different types of scenarios. A proof of consistency is also provided. Our methodology is implemented in the R package wbsts, available from CRAN.

Item Type: Article
Official URL: http://www3.stat.sinica.edu.tw/statistica/
Additional Information: © 2017 Institute of Statistical Science, Academia Sinica
Divisions: Statistics
Subjects: Q Science > QA Mathematics
Sets: Departments > Statistics
Date Deposited: 07 Jan 2016 15:09
Last Modified: 20 Jun 2020 02:16
URI: http://eprints.lse.ac.uk/id/eprint/64863

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