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Regular version of the site

Book chapter

Nonparametric decomposition of quasi-periodic time series for change-point detection

P. 987520-987525.
Artemov A., Burnaev E., Lokot A.

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.

In book

Edited by: A. Verikas, P. Radeva, D. Nikolaev. Barcelona: SPIE, 2015.