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

TL;DR

This paper introduces SUBSET, a computationally efficient, model-based method for detecting multiple changepoints in high-dimensional multivariate time series, with applications to global terrorism data, demonstrating superior performance over existing methods.

Contribution

The paper presents SUBSET, a novel penalised likelihood approach for high-dimensional changepoint detection, with theoretical guidance and applicability to non-Gaussian data.

Findings

SUBSET outperforms existing methods in Gaussian mean change detection.

It is adaptable to non-Gaussian data like count models.

The method is computationally efficient for high-dimensional data.

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.