A computationally efficient, high-dimensional multiple changepoint procedure with application to global terrorism incidence

Tickle, S. O. and Eckley, I. A. and Fearnhead, P. (2021) A computationally efficient, high-dimensional multiple changepoint procedure with application to global terrorism incidence. Journal of the Royal Statistical Society: Series A Statistics in Society. ISSN 0964-1998 (In Press)

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Abstract

Detecting changepoints in datasets with many variates is a data science challenge of increasing importance. Motivated by the problem of detecting changes in the incidence of terrorism from a global terrorism database, we propose a novel approach to multiple changepoint detection in multivariate time series. Our method, which we call SUBSET, is a model-based approach which uses a penalised likelihood to detect changes for a wide class of parametric settings. We provide theory that guides the choice of penalties to use for SUBSET, and that shows it has high power to detect changes regardless of whether only a few variates or many variates change. Empirical results show that SUBSET out-performs many existing approaches for detecting changes in mean in Gaussian data; additionally, unlike these alternative methods, it can be easily extended to non-Gaussian settings such as are appropriate for modelling counts of terrorist events.

Item Type:
Journal Article
Journal or Publication Title:
Journal of the Royal Statistical Society: Series A Statistics in Society
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1800/1804
Subjects:
ID Code:
149682
Deposited By:
Deposited On:
07 Dec 2020 12:05
Refereed?:
Yes
Published?:
In Press
Last Modified:
26 Jun 2021 04:41