Open Access
December 2013 Hidden Markov models for the activity profile of terrorist groups
Vasanthan Raghavan, Aram Galstyan, Alexander G. Tartakovsky
Ann. Appl. Stat. 7(4): 2402-2430 (December 2013). DOI: 10.1214/13-AOAS682


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.


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Vasanthan Raghavan. Aram Galstyan. Alexander G. Tartakovsky. "Hidden Markov models for the activity profile of terrorist groups." Ann. Appl. Stat. 7 (4) 2402 - 2430, December 2013.


Published: December 2013
First available in Project Euclid: 23 December 2013

zbMATH: 1283.62244
MathSciNet: MR3161728
Digital Object Identifier: 10.1214/13-AOAS682

Keywords: Colombia , Hidden Markov model , Indonesia , Peru , point process , self-exciting hurdle model , spurt detection , terrorism , terrorist groups

Rights: Copyright © 2013 Institute of Mathematical Statistics

Vol.7 • No. 4 • December 2013
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