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

Alexey Artemov; Evgeny Burnaev; Andrey Lokot

doi:10.1117/12.2228370

8 December 2015 Nonparametric decomposition of quasi-periodic time series for change-point detection

Alexey Artemov, Evgeny Burnaev, Andrey Lokot

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.

Citation Download Citation

Alexey Artemov, Evgeny Burnaev, and 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); https://doi.org/10.1117/12.2228370

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available