A cusum change-point detection algorithm for non-stationary sequences with application to data network surveillance

We adapt the classic cusum change-point detection algorithm to handle non-stationary sequences that are typical with network surveillance applications. The proposed algorithm uses a defined timeslot structure to take into account time varying distributions, and uses historical samples of observations within each timeslot to facilitate a nonparametric methodology. Our proposed solution includes an on-line screening feature that fully automates the implementation of the algorithm and eliminates the need for manual oversight up until the point where root cause analysis begins.