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 Jg. 68; S. 3515 - 3528
Hauptverfasser:	Keriven, Nicolas, Garreau, Damien, Poli, Iacopo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.01.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers
Schlagworte:	Algorithms Brain modeling Change detection Change detection algorithms Computational modeling Hilbert space Kernel Machine Learning method of moments Microsoft Windows optical computing Radio frequency Signal processing algorithms Statistical methods Statistics Streaming media Radio frequency Microsoft Windows Computational modeling Signal processing algorithms Streaming media Brain modeling Kernel
ISSN:	1053-587X, 1941-0476
Online-Zugang:	Volltext
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