Wild binary segmentation for multiple change-point detection
TL;DR: Wild binary segmentation (WBS) as discussed by the authors is a new technique for consistent estimation of the number and locations of multiple change-points in data, which does not require the choice of a window or span parameter and does not lead to a significant increase in computational complexity.
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Abstract: We propose a new technique, called wild binary segmentation (WBS), for consistent estimation of the number and locations of multiple change-points in data. We assume that the number of change-points can increase to infinity with the sample size. Due to a certain random localisation mechanism, WBS works even for very short spacings between the change-points and/or very small jump magnitudes, unlike standard binary segmentation. On the other hand, despite its use of localisation, WBS does not require the choice of a window or span parameter, and does not lead to a significant increase in computational complexity. WBS is also easy to code. We propose two stopping criteria for WBS: one based on thresholding and the other based on what we term the ‘strengthened Schwarz information criterion’. We provide default recommended values of the parameters of the procedure and show that it offers very good practical performance in comparison with the state of the art. The WBS methodology is implemented in the R package wbs, available on CRAN. In addition, we provide a new proof of consistency of binary segmentation with improved rates of convergence, as well as a corresponding result for WBS.
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Citations
Selective review of offline change point detection methods
TL;DR: In this article, the authors present a selective survey of algorithms for the offline detection of multiple change points in multivariate time series, and a general yet structuring methodological strategy is adopted to organize this vast body of work.
953
Wild binary segmentation for multiple change-point detection
TL;DR: Wild binary segmentation (WBS) as discussed by the authors is a new technique for consistent estimation of the number and locations of multiple change-points in data, which does not require the choice of a window or span parameter and does not lead to a significant increase in computational complexity.
Multiscale change point inference
TL;DR: A new estimator, the simultaneous multiscale change point estimator SMUCE, is introduced, which achieves the optimal detection rate of vanishing signals as n→∞, even for an unbounded number of change points.
423
Multiple-change-point detection for high dimensional time series via sparsified binary segmentation
Haeran Cho,Piotr Fryzlewicz +1 more
TL;DR: Sparsified Binary Segmentation (SBS) as mentioned in this paper combines the CUSUM statistics obtained from local periodograms and cross-periodograms of the components of the input time series to reduce the impact of irrelevant, noisy contributions.
Multiple-change-point detection for high dimensional time series via sparsified binary segmentation
Haeran Cho,Piotr Fryzlewicz +1 more
TL;DR: This work proposes the sparsified binary segmentation algorithm which aggregates the cumulative sum statistics by adding only those that pass a certain threshold, which reduces the influence of irrelevant noisy contributions, which is particularly beneficial in high dimensions.
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