  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.
