PARC: Learning Historical References for Online Change-Point Detection

Online change-point detection is challenging when no part of the observed sequence is known to follow the pre-change distribution. Waiting for a long reference can delay detection, while limited history may poorly characterize normal distributional variation. A large learned contrast need not exceed historical fluctuations in the same direction. We propose PARC, a rolling detector that estimates proposal-conditioned historical response distributions. Each proposal is defined by a recent-versus-earlier contrast at a particular time and scale, describing how the distribution may be changing. Conditioned on that proposal, PARC estimates the corresponding historical response distribution and learns how much that type of variation has occurred before. Different candidate changes are therefore evaluated against different historical variability. New proposals update the collection of references, while each comparison is fixed before later observations are tested. We analyze score sensitivity, the effect of startup on later thresholds, and a separate finite-horizon false-alarm guarantee under exchangeable normal sequences. Across two complete archives, PARC achieves higher detection coverage and lower capped detection loss than twelve evaluated baselines, with fewer early alarms than the strongest detection comparator on each archive under a matched protocol.