| Summary: | Activity profiles of terrorist groups show frequent spurts and downfalls corresponding to changes in the underlying organizational dynamics. In particular, it is of interest in understanding changes in attributes such as intentions/ideology, tactics/strategies, capabilities/resources, etc., that influence and impact the activity. The goal of this work is the quick detection of such changes and in general, tracking of macroscopic as well as microscopic trends in group dynamics. Prior work in this area are based on parametric approaches and rely on time-series analysis techniques, self-exciting hurdle models (SEHM), or hidden Markov models (HMM). While these approaches detect spurts and downfalls reasonably accurately, they are all based on model learning --- a task that is difficult in practice because of the "rare" nature of terrorist attacks from a model learning perspective. In this paper, we pursue an alternate non-parametric approach for spurt detection in activity profiles. Our approach is based on binning the count data of terrorist activity to form observation vectors that can be compared with each other. Motivated by a majorization theory framework, these vectors are then transformed via certain functionals and used in spurt classification. While the parametric approaches often result in either a large number of missed detections of real changes or false alarms of unoccurred changes, the proposed approach is shown to result in a small number of missed detections and false alarms. Further, the non-parametric nature of the approach makes it attractive for ready applications in a practical context.Comment: 22 pages + 14 pages supplementary material, 6 figure
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