Hidden Markov models for the activity profile of terrorist groups

Abstract

The main focus of this work is on developing models for the activity profile of a terrorist group, detecting sudden spurts and downfalls in this profile, and, in general, tracking it over a period of time. Toward this goal, a $d$-state hidden Markov model (HMM) that captures the latent states underlying the dynamics of the group and thus its activity profile is developed. The simplest setting of $d=2$ corresponds to the case where the dynamics are coarsely quantized as Active and Inactive, respectively. A state estimation strategy that exploits the underlying HMM structure is then developed for spurt detection and tracking. This strategy is shown to track even nonpersistent changes that last only for a short duration at the cost of learning the underlying model. Case studies with real terrorism data from open-source databases are provided to illustrate the performance of the proposed methodology.

Published in at http://dx.doi.org/10.1214/13-AOAS682 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)

References in corpus (4)

• On the Frequency of Severe Terrorist Events
• Self-exciting hurdle models for terrorist activity
• Hidden Markov models for the activity profile of terrorist groups
• Latent Self-Exciting Point Process Model for Spatial-Temporal Networks

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• Discussion of "Estimating the historical and future probabilities of large terrorist events" by Aaron Clauset and Ryan Woodard
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