Partially-Observable Sequential Change-Point Detection for Autocorrelated Data via Upper Confidence Region

arXiv:2404.00220v1 Announce Type: cross
Abstract: Sequential change point detection for multivariate autocorrelated data is a very common problem in practice. However, when the sensing resources are limited, only a subset of variables from the multivariate system can be observed at each sensing time point. This raises the problem of partially observable multi-sensor sequential change point detection. For it, we propose a detection scheme called adaptive upper confidence region with state space model (AUCRSS). It models multivariate time series via a …

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