论文标题
在全球恐怖主义发病率上应用的计算效率高，高维的多重变更点程序
A computationally efficient, high-dimensional multiple changepoint procedure with application to global terrorism incidence
论文作者
论文摘要
检测具有许多变体的数据集中的变更点是重要性的数据科学挑战。由于全球恐怖主义数据库发现恐怖主义发生率的变化的问题，我们提出了一种新颖的方法，用于多变量时间序列中的多个变更点检测。我们称为子集的方法是一种基于模型的方法，它使用惩罚可能性来检测广泛的参数设置的更改。我们提供了指导子集使用的惩罚选择的理论，并且表明它具有很高的能力来检测变化，而不管仅几个变体或许多变体变化。经验结果表明，子集超出了许多现有方法来检测高斯数据中平均值的变化。此外，与这些替代方法不同，它可以很容易地扩展到非高斯环境，例如适合对恐怖事件进行建模。
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.
