On optimal multiple changepoint algorithms for large data

Maidstone, Robert; Hocking, Toby Dylan; Rigaill, Guillem; Fearnhead, Paul

doi:10.1007/s11222-016-9636-3

Cited by 161 publications

(183 citation statements)

References 26 publications

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“…Killick et al (2012) introduced the pruned exact linear time (PELT) method, which has the worst case computational cost of order O(n 2 ); while in the situations where the number of change points increases linearly with n, the expected time of PELT is of order O(n). There are also other algorithms, including Rigaill (2010) and Maidstone et al (2017), which have been shown to have an expected cost which is smaller than that of PELT, but which have the worst case cost also of order O(n 2 ).…”

Section: Introductionmentioning

confidence: 99%

Univariate mean change point detection: Penalization, CUSUM and optimality

Wang¹,

Yu²,

Rinaldo³

2020

Electron. J. Statist.

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The problem of univariate mean change point detection and localization based on a sequence of n independent observations with piecewise constant means has been intensively studied for more than half century, and serves as a blueprint for change point problems in more complex settings. We provide a complete characterization of this classical problem in a general framework in which the upper bound σ 2 on the noise variance, the minimal spacing ∆ between two consecutive change points and the minimal magnitude κ of the changes, are allowed to vary with n. We first show that consistent localization of the change points, when the signal-to-noise ratio κdiverges with n at the rate of at least log(n), we demonstrate that two computationally-efficient change point estimators, one based on the solution to an ℓ 0 -penalized least squares problem and the other on the popular wild binary segmentation algorithm, are both consistent and achieve a localization rate of the order σ 2 κ 2 log(n). We further show that such rate is minimax optimal, up to a log(n) term.

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Section: Introductionmentioning

confidence: 99%

Univariate mean change point detection: Penalization, CUSUM and optimality

Wang¹,

Yu²,

Rinaldo³

2020

Electron. J. Statist.

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“…Precisely, for two indexes t and s (t < s < T ), the pruning rule is given by: An extension of Pelt is described in [9] to solve the linearly penalized change point detection for a range of smoothing parameter values [β min , β max ]. Pelt has been applied on DNA sequences [16,17], physiological signals [89], and oceanographic data [111].…”

Section: Solution To Problem 2 (P2): Peltmentioning

confidence: 99%

Selective review of offline change point detection methods

2020

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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 is adopted to organize this vast body of work. More precisely, detection algorithms considered in this review are characterized by three elements: a cost function, a search method and a constraint on the number of changes. Each of those elements is described, reviewed and discussed separately. Implementations of the main algorithms described in this article are provided within a Python package called ruptures.

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“…Indeed, the inference of these parameters requires to visit the whole segmentation space, which is prohibitive in terms of computational time when the visit is performed in a naive way. The Dynamic Programming (DP) algorithm (introduced by [16] and used for the first time in segmentation by [17]) and, recently its pruned versions [18,19,20], is the only efficient algorithm that retrieves the exact solution (i.e. the optimal segmentation according to the log-likelihood or least-square contrasts for example) in a faster way.…”

Section: Introductionmentioning

confidence: 99%

A breakpoint detection in the mean model with heterogeneous variance on fixed time intervals

et al. 2019

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This work is motivated by an application for the homogeneization of GNSS-derived IWV (Integrated Water Vapour) series. Indeed, these GPS series are affected by abrupt changes due to equipment changes or environemental effects. The detection and correction of the series from these changes is a crucial step before any use for climate studies. In addition to these abrupt changes, it has been observed in the series a non-stationary of the variability. We propose in this paper a new segmentation model that is a breakpoint detection in the mean model of a Gaussian process with heterogeneous variance on known time-intervals. In this segmentation case, the dynamic programming (DP) algorithm used classically to infer the breakpoints can not be applied anymore. We propose a procedure in two steps: we first estimate robustly the variances and then apply the classical inference by plugging these estimators.

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On optimal multiple changepoint algorithms for large data

Cited by 161 publications

References 26 publications

Univariate mean change point detection: Penalization, CUSUM and optimality

Univariate mean change point detection: Penalization, CUSUM and optimality

Selective review of offline change point detection methods

A breakpoint detection in the mean model with heterogeneous variance on fixed time intervals

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