Hidden Markov models for the activity profile of terrorist groups. (English) Zbl 1283.62244

Summary: 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 data bases are provided to illustrate the performance of the proposed methodology.


62P99 Applications of statistics
62M99 Inference from stochastic processes
91F10 History, political science
65C60 Computational problems in statistics (MSC2010)
Full Text: DOI arXiv Euclid


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