8 December 2015 Nonparametric decomposition of quasi-periodic time series for change-point detection
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Proceedings Volume 9875, Eighth International Conference on Machine Vision (ICMV 2015); 987520 (2015) https://doi.org/10.1117/12.2228370
Event: Eighth International Conference on Machine Vision, 2015, Barcelona, Spain
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.
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Alexey Artemov, Alexey Artemov, Evgeny Burnaev, Evgeny Burnaev, Andrey Lokot, Andrey Lokot, } "Nonparametric decomposition of quasi-periodic time series for change-point detection", Proc. SPIE 9875, Eighth International Conference on Machine Vision (ICMV 2015), 987520 (8 December 2015); doi: 10.1117/12.2228370; https://doi.org/10.1117/12.2228370
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