Hidden Markov models for the activity profile of terrorist groups
Preprint, Other literature type
Tartakovsky, Alexander G.
- Publisher: The Institute of Mathematical Statistics
Computer Science - Social and Information Networks | Physics - Physics and Society | self-exciting hurdle model | 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.