Robust Quickest Change Detection in Statistically Periodic Processes

The problem of detecting a change in the distribution of a statistically periodic process is investigated. The problem is posed in the framework of independent and periodically identically distributed (i.p.i.d.) processes, a recently introduced class of processes to model statistically periodic data. An algorithm is proposed that is shown to be robust against an uncertainty in the post-change law. The motivation for the problem comes from event detection problems in traffic data, social network data, electrocardiogram data, and neural data, where periodic statistical behavior has been observed.