Bayesian Model for Multiple Change-points Detection in Multivariate Time Series

2014/07/11 by Flore Harlé, Harlé, Flore, Florent Chatelain +6 · 1 citation
Biochemistry, Genetics and Molecular Biology · Chemistry · Mathematics · #FOS: Computer and information sciences #FOS: Mathematics #Metabolomics and Mass Spectrometry Studies #Methodology (stat.ME) #Spectroscopy and Chemometric Analyses #Statistical Methods and Inference #Statistics Theory (math.ST) #math.ST #stat.ME #stat.TH

paper · pdf · doi:10.48550/arxiv.1407.3206

29 pages, 27 figures

arxiv created 2014/07/11 · openalex publication_date 2014/07/11 · arxiv updated 2014/07/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This paper addresses the issue of detecting change-points in multivariate time series. The proposed approach differs from existing counterparts by making only weak assumptions on both the change-points structure across series, and the statistical signal distributions. Specifically change-points are not assumed to occur at simultaneous time instants across series, and no specific distribution is assumed on the individual signals. It relies on the combination of a local robust statistical test acting on individual time segments, with a global Bayesian framework able to optimize configurations from multiple local statistics (from segments of a unique time series or multiple time series). Using an extensive experimental set-up, our algorithm is shown to perform well on Gaussian data, with the same results in term of recall and precision as classical approaches, such as the fused lasso and the Bernoulli Gaussian model. Furthermore, it outperforms the reference models in the case of non normal data with outliers. The control of the False Discovery Rate by an acceptance level is confirmed. In the case of multivariate data, the probabilities that simultaneous change-points are shared by some specific time series are learned. We finally illustrate our algorithm with real datasets from energy monitoring and genomic. Segmentations are compared to state-of-the-art approaches based on fused lasso and group fused lasso.

Citations

• The control of the false discovery rate in multiple testing under dependency
• The group fused Lasso for multiple change-point detection
• Homogeneity and change-point detection tests for multivariate data using rank statistics
• Exact and efficient Bayesian inference for multiple changepoint problems
• On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other
• Calibration ofρValues for Testing Precise Null Hypotheses
• Sparse/Robust Estimation and Kalman Smoothing with Nonsmooth Log-Concave Densities: Modeling, Computation, and Theory
• ecp: An R Package for Nonparametric Multiple Change Point Analysis of Multivariate Data

Cited by

• Network Modeling of Asynchronous Change-Points in Multivariate Time Series

Related

• An Encoding Approach for Stable Change Point Detection
• Bayesian detection of abnormal segments in multiple time series
• Nonparametric Multiple Change Point Detection for Non-Stationary Times Series
• Multiple Changepoint Detection with Partial Information on Changepoint Times
• Testing for a Change in Mean After Changepoint Detection