Adaptive Upper Confidence Region Approach for Partially-Observable Sequential Change-Point Detection in Autocorrelated Multivariate Data Streams

A novel adaptive upper confidence region approach (AUCRSS) is proposed to efficiently detect change points in partially-observable autocorrelated multivariate data streams by leveraging state space modeling, generalized likelihood ratio testing, and combinatorial multi-armed bandit optimization.