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Rank-based multiple change-point detection in multivariate time series

Flore Harlé 1, 2 Florent Chatelain 3, 2 Cedric Gouy-Pailler 1 Sophie Achard 4, 2 
1 LIMA - Laboratoire Information, Modèles, Apprentissage [Gif-sur-Yvette]
DM2I - Département Métrologie Instrumentation & Information : DRT/LIST/DM2I
3 GIPSA-SAIGA - GIPSA - Signal et Automatique pour la surveillance, le diagnostic et la biomécanique
GIPSA-DA - Département Automatique, GIPSA-DIS - Département Images et Signal
Abstract : In this paper, we propose a Bayesian approach for multivariate time series segmentation. A robust non-parametric test, based on rank statistics, is derived in a Bayesian framework to yield robust distribution-independent segmentations of piecewise constant multivariate time series for which mutual dependencies are unknown. By modelling rank-test p-values, a pseudo-likelihood is proposed to favour change-points detection for significant p-values. A vague prior is chosen for dependency structure between time series, and a MCMC method is applied to the resulting posterior distribution. The Gibbs sampling strategy makes the method computationally efficient. The algorithm is illustrated on simulated and real signals in two practical settings. It is demonstrated that change-points are robustly detected and localized, through implicit dependency structure learning or explicit structural prior introduction.
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Submitted on : Monday, June 13, 2022 - 11:54:31 AM
Last modification on : Tuesday, June 14, 2022 - 10:28:27 AM


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  • HAL Id : cea-01844442, version 1


Flore Harlé, Florent Chatelain, Cedric Gouy-Pailler, Sophie Achard. Rank-based multiple change-point detection in multivariate time series. EUSIPCO 2014 - 22th European Signal Processing Conference, Sep 2014, Lisbonne, Portugal. pp.1337-1341. ⟨cea-01844442⟩



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