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

21 May 2018Nicolas KerivenDamien GarreauIacopo Poli

We consider the problem of detecting abrupt changes in the distribution of a multi-dimensional time series, with limited computing power and memory. In this paper, we propose a new, simple method for model-free online change-point detection that relies only on fast and light recursive statistics, inspired by the classical Exponential Weighted Moving Average algorithm (EWMA)... (read more)

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