Change-Point Detection

Change-point detection studies when and where a time series, data stream, or evolving system shifts between regimes, helping people respond to failures, drift, or changing conditions. Offline methods segment data using dynamic programming and penalized costs, while online methods such as CUSUM, EWMA, and martingale tests trade detection speed against false alarms; Bayesian, kernel, graph-based, and neural approaches handle richer data. Work spans settings from multivariate monitoring and dynamic networks to deployed machine-learning models, with growing attention to confidence sets, attribution, and early-warning evaluation. A central challenge is maintaining reliable guarantees under dependence, adaptation, and unknown shifts without making detection too slow or too prone to alarms.