A Computationally Efficient, High-Dimensional Multiple Changepoint Procedure with Application to Global Terrorism Incidence
提出一种名为SUBSET的模型方法，用于检测多变量时间序列中的多个变点，在计算上高效且适用于高斯和非高斯数据，在全球恐怖主义事件数据中表现优于现有方法。
Abstract Detecting changepoints in data sets 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.