A Binary Prototype for Time-Series Surveillance and Intervention.

Despite much research on early detection of anomalies from surveillance data, a systematic framework for appropriately acting on these signals is lacking. We addressed this gap by formulating a hidden Markov-style model for time-series surveillance, where the system state, the observed data, and the decision rule are all binary. We incur a delayed cost, , whenever the system is abnormal and no action is taken, or an immediate cost, , with action, where .