Abstract

Changepoint detection has received considerable attention in recent years, with a myriad of methods arising to address a diverse range of data-intensive situations from genomics to telecommunications. In particular, there now exist techniques for streaming data; high-dimensional settings; and instances in which a parametric model cannot be known in advance. However, there are relatively few contributions which attempt to address all three of these problems simultaneously. We’ll discuss these, alongside a new such method called OMEN ; we will talk about the theoretical and empirical properties of OMEN as compared with a suite of standard offline change detectors, before examining the method’s performance on the Global Terrorism Database (GTD). The GTD , copyrighted to the University of Maryland, and maintained by the National Consortium of the Study of Terrorism and Responses to Terrorism (START), catalogues terror activity from 1970 to the present. We will use this resource to pass comment on moments in the past fifty years in which the probability of terror activity appeared to shift abruptly, using OMEN to attempt to distinguish between national, regional and global effects.