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.

中文翻译：

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一种适用于全球恐怖主义事件的计算高效、高维多变点程序

检测具有许多变量的数据集中的变化点是一项越来越重要的数据科学挑战。受从全球恐怖主义数据库中检测恐怖主义发生率变化问题的启发，我们提出了一种在多变量时间序列中检测多个变化点的新方法。我们的方法，我们称之为 SUBSET，是一种基于模型的方法，它使用惩罚可能性来检测各种参数设置的变化。我们提供了指导选择用于 SUBSET 的惩罚的理论，并表明无论是只有少数变量还是许多变量发生变化，它都有很强的检测变化的能力。实证结果表明，SUBSET 优于许多现有的检测高斯数据均值变化的方法；此外，与这些替代方法不同的是，