Rights statement: This is the peer reviewed version of the following article: S. O. Tickle, I. A. Eckley, P. Fearnhead (2021), A computationally efficient, high-dimensional multiple changepoint procedure with application to global terrorism incidence. Journal of the Royal Statistical Society: Statistics in society: Series A. doi: 10.1111/rssa.12695 which has been published in final form at https://rss.onlinelibrary.wiley.com/doi/10.1111/rssa.12695 This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.
Accepted author manuscript, 2.33 MB, PDF document
Available under license: CC BY: Creative Commons Attribution 4.0 International License
Final published version
Licence: CC BY: Creative Commons Attribution 4.0 International License
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - A computationally efficient, high-dimensional multiple changepoint procedure with application to global terrorism incidence
AU - Tickle, S. O.
AU - Eckley, I. A.
AU - Fearnhead, P.
N1 - This is the peer reviewed version of the following article: S. O. Tickle, I. A. Eckley, P. Fearnhead (2021), A computationally efficient, high-dimensional multiple changepoint procedure with application to global terrorism incidence. Journal of the Royal Statistical Society: Statistics in society: Series A. doi: 10.1111/rssa.12695 which has been published in final form at https://rss.onlinelibrary.wiley.com/doi/10.1111/rssa.12695 This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.
PY - 2021/10/31
Y1 - 2021/10/31
N2 - 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.
AB - 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.
KW - binary segmentation
KW - Likelihood ratio
KW - Multivariate changepoint detection
KW - Penalised cost function
KW - Wild binary segmentation
U2 - 10.1111/rssa.12695
DO - 10.1111/rssa.12695
M3 - Journal article
VL - 184
SP - 1303
EP - 1325
JO - Journal of the Royal Statistical Society: Series A Statistics in Society
JF - Journal of the Royal Statistical Society: Series A Statistics in Society
SN - 0964-1998
IS - 4
ER -