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Proceedings Paper

Nonparametric decomposition of quasi-periodic time series for change-point detection
Author(s): Alexey Artemov; Evgeny Burnaev; Andrey Lokot
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Paper Abstract

The paper is concerned with the sequential online change-point detection problem for a dynamical system driven by a quasiperiodic stochastic process. We propose a multicomponent time series model and an effective online decomposition algorithm to approximate the components of the models. Assuming the stationarity of the obtained components, we approach the change-point detection problem on a per-component basis and propose two online change-point detection schemes corresponding to two real-world scenarios. Experimental results for decomposition and detection algorithms for synthesized and real-world datasets are provided to demonstrate the efficiency of our change-point detection framework.

Paper Details

Date Published: 8 December 2015
PDF: 5 pages
Proc. SPIE 9875, Eighth International Conference on Machine Vision (ICMV 2015), 987520 (8 December 2015); doi: 10.1117/12.2228370
Show Author Affiliations
Alexey Artemov, Moscow State Univ. (Russian Federation)
Yandex Data Factory (Russian Federation)
Evgeny Burnaev, Institute for Information Transmission Problems (Russian Federation)
Andrey Lokot, Yandex Data Factory (Russian Federation)

Published in SPIE Proceedings Vol. 9875:
Eighth International Conference on Machine Vision (ICMV 2015)
Antanas Verikas; Petia Radeva; Dmitry Nikolaev, Editor(s)

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