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We consider the problem of detecting abrupt changes in the distribution of amulti-dimensional time series, with limited computing power and memory. In thispaper, we propose a new, simple method for model-free online change-pointdetection that relies only on fast and light recursive statistics, inspired bythe classical Exponential Weighted Moving Average algorithm (EWMA). Theproposed idea is to compute two EWMA statistics on the stream of data withdifferent forgetting factors, and to compare them. By doing so, we show that weimplicitly compare recent samples with older ones, without the need toexplicitly store them. Additionally, we leverage Random Features (RFs) toefficiently use the Maximum Mean Discrepancy as a distance betweendistributions, furthermore exploiting recent optical hardware to computehigh-dimensional RFs in near constant time. We show that our method issignificantly faster than usual non-parametric methods for a given accuracy.

NEWMA: a new method for scalable model-free online change-point detection