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Multiple-change-point detection for high dimensional time series via sparsified binary segmentation

Cho, Haeran and Fryzlewicz, Piotr ORCID: 0000-0002-9676-902X (2015) Multiple-change-point detection for high dimensional time series via sparsified binary segmentation. Journal of the Royal Statistical Society. Series B: Statistical Methodology, 77 (2). 475 - 507. ISSN 1369-7412

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Identification Number: 10.1111/rssb.12079


Time series segmentation, which is also known as multiple-change-point detection, is a well-established problem. However, few solutions have been designed specifically for high dimensional situations. Our interest is in segmenting the second-order structure of a high dimensional time series. In a generic step of a binary segmentation algorithm for multivariate time series, one natural solution is to combine cumulative sum statistics obtained from local periodograms and cross-periodograms of the components of the input time series. However, the standard 'maximum' and 'average' methods for doing so often fail in high dimensions when, for example, the change points are sparse across the panel or the cumulative sum statistics are spuriously large. We propose the sparsified binary segmentation algorithm which aggregates the cumulative sum statistics by adding only those that pass a certain threshold. This 'sparsifying' step reduces the influence of irrelevant noisy contributions, which is particularly beneficial in high dimensions. To show the consistency of sparsified binary segmentation, we introduce the multivariate locally stationary wavelet model for time series, which is a separate contribution of this work.

Item Type: Article
Official URL:
Additional Information: © 2014 Royal Statistical Society
Divisions: Statistics
Subjects: H Social Sciences > HA Statistics
Date Deposited: 23 Jun 2014 15:47
Last Modified: 20 Oct 2021 02:10

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