Hidden Markov Models for the Activity Profile of Terrorist Groups

نویسندگان

  • Vasanthan Raghavan
  • Aram Galstyan
  • Alexander G. Tartakovsky
چکیده

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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عنوان ژورنال:
  • CoRR

دوره abs/1207.1497  شماره 

صفحات  -

تاریخ انتشار 2012