This article presents a selective survey of algorithms for the offline detection of multiple
change points in multivariate time series. A general yet structuring methodological strategy …

A general framework for modeling dependence in multivariate time series is presented. Its
fundamental approach relies on decomposing each signal inside a system into various …

Data segmentation aka multiple change point analysis has received considerable attention
due to its importance in time series analysis and signal processing, with applications in a …

Due to the exponential growth of the Internet of Things networks and the massive amount of
time series data collected from these networks, it is essential to apply efficient methods for …

This article presents a selective survey of algorithms for the offline detection of multiple
change points in multivariate time series. A general yet structuring methodological strategy …

Appendix A: Proofs of main results. The proofs of Propositions 2.1, 2.2, Theorems 3.1, 4.1,
4.2, 5.1, 5.2, Corollaries 5.3, 5.4 and Corollaries 6.1, 6.2 are placed in Sections A. 1–A. 12 …

A new class of change point test statistics is proposed that utilizes a weighting and trimming
scheme for the cumulative sum (CUSUM) process inspired by Rényi. A thorough asymptotic …

Many changepoint detection procedures rely on the estimation of nuisance parameters (like
long-run variance). If a change has occurred, estimators might be biased and data adaptive …

Eugene Fama once mentioned in 2016 that people have not come up with ways of
identifying bubbles statistically. This paper presents the nonparametric change-point method …

Appendix A: Proofs of main results. The proofs of Propositions 3.1, 4.4, Theorems 3.2, 4.1,
4.3, 4.5, 4.6, 4.7, Corollaries 4.2, 4.8 and (11) are placed in Sections A. 1–A. 11 …