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Raghavan, Vasanthan; Galstyan, Aram; Tartakovsky, Alexander G. (2012)
Publisher: The Institute of Mathematical Statistics
Languages: English
Types: Article
Subjects: Computer Science - Social and Information Networks, self-exciting hurdle model, Physics - Physics and Society, Peru, Indonesia, Statistics - Applications, terrorist groups, spurt detection, Physics - Data Analysis, Statistics and Probability, point process, terrorism, Hidden Markov model, Colombia
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.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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    The results below are discovered through our pilot algorithms. Let us know how we are doing!

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