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Abstract:
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We consider the problem of modeling the site selection behavior of criminal and terrorist actors. These site selection processes are non-stationary and can exhibit slow to abrupt changes (e.g. due to police and military actions). Such processes also complicate feature selection as different features may be important at different times. Furthermore, timely responses are desired necessitating a model that can rapidly assess new data and make relevant predictions, incorporating the possibility of radical shifts in the actors' tactics. This paper describes the use of the fused lasso to model these dynamic processes over time. We develop a sequential fused lasso algorithm that incorporates automatic feature selection, adaptation to process changes, and discovery of change points.
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