Hidden Markov models for the activity profile of terrorist groups

TL;DR

This paper develops a hidden Markov model to analyze and track the activity patterns of terrorist groups, detecting sudden changes and providing real-data case studies.

Contribution

It introduces a $d$-state HMM framework for modeling terrorist activity profiles, including a state estimation method for detecting activity spurts and declines.

Findings

Successfully detects short-term activity changes

Effective in real terrorism data case studies

Provides a systematic approach for activity profile analysis

Abstract

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.