NEWMA: A New Method for Scalable Model-Free Online Change-Point Detection

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, in...

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Veröffentlicht in:	IEEE transactions on signal processing 2020-01, Vol.68, p.3515-3528
Hauptverfasser:	Keriven, Nicolas, Garreau, Damien, Poli, Iacopo
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Brain modeling Change detection Change detection algorithms Computational modeling Engineering Engineering, Electrical & Electronic Hilbert space Kernel Machine Learning method of moments Microsoft Windows optical computing Radio frequency Science & Technology Signal processing algorithms Statistical methods Statistics Streaming media Technology
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