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cpop: Detecting changes in piecewise-linear signals

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cpop: Detecting changes in piecewise-linear signals. / Grose, Daniel; Fearnhead, Paul.
In: Journal of Statistical Software, Vol. 109, No. 7, 7, 29.05.2024, p. 1-30.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Grose, D & Fearnhead, P 2024, 'cpop: Detecting changes in piecewise-linear signals', Journal of Statistical Software, vol. 109, no. 7, 7, pp. 1-30. https://doi.org/10.18637/jss.v109.i07

APA

Grose, D., & Fearnhead, P. (2024). cpop: Detecting changes in piecewise-linear signals. Journal of Statistical Software, 109(7), 1-30. Article 7. https://doi.org/10.18637/jss.v109.i07

Vancouver

Grose D, Fearnhead P. cpop: Detecting changes in piecewise-linear signals. Journal of Statistical Software. 2024 May 29;109(7):1-30. 7. doi: 10.18637/jss.v109.i07

Author

Grose, Daniel ; Fearnhead, Paul. / cpop: Detecting changes in piecewise-linear signals. In: Journal of Statistical Software. 2024 ; Vol. 109, No. 7. pp. 1-30.

Bibtex

@article{4beda7d6d8ba42888c3e2efcb4a95239,
title = "cpop: Detecting changes in piecewise-linear signals",
abstract = "Changepoint detection is an important problem with applications across many application domains. There are many different types of changes that one may wish to detect, and a wide-range of algorithms and software for detecting them. However there are relatively few approaches for detecting changes-in-slope in the mean of a signal plus noise model. We describe the R package, cpop, available on the Comprehensive R Archive Network (CRAN). This package implements CPOP, a dynamic programming algorithm, to find the optimal set of changes that minimises an L_0 penalised cost, with the cost being a weighted residual sum of squares. The package has extended the CPOP algorithm so it can analyse data that is unevenly spaced, allow for heterogeneous noise variance, and allows for a grid of potential change locations to be different from the locations of the data points. There is also an implementation that uses the CROPS algorithm to detect all segmentations that are optimal as you vary the L_0 penalty for adding a change across a continuous range of values. ",
keywords = "changepoints, change-in-slope, dynamic programming, piecewise linear models, structural breaks",
author = "Daniel Grose and Paul Fearnhead",
year = "2024",
month = may,
day = "29",
doi = "10.18637/jss.v109.i07",
language = "English",
volume = "109",
pages = "1--30",
journal = "Journal of Statistical Software",
issn = "1548-7660",
publisher = "University of California at Los Angeles",
number = "7",

}

RIS

TY - JOUR

T1 - cpop: Detecting changes in piecewise-linear signals

AU - Grose, Daniel

AU - Fearnhead, Paul

PY - 2024/5/29

Y1 - 2024/5/29

N2 - Changepoint detection is an important problem with applications across many application domains. There are many different types of changes that one may wish to detect, and a wide-range of algorithms and software for detecting them. However there are relatively few approaches for detecting changes-in-slope in the mean of a signal plus noise model. We describe the R package, cpop, available on the Comprehensive R Archive Network (CRAN). This package implements CPOP, a dynamic programming algorithm, to find the optimal set of changes that minimises an L_0 penalised cost, with the cost being a weighted residual sum of squares. The package has extended the CPOP algorithm so it can analyse data that is unevenly spaced, allow for heterogeneous noise variance, and allows for a grid of potential change locations to be different from the locations of the data points. There is also an implementation that uses the CROPS algorithm to detect all segmentations that are optimal as you vary the L_0 penalty for adding a change across a continuous range of values.

AB - Changepoint detection is an important problem with applications across many application domains. There are many different types of changes that one may wish to detect, and a wide-range of algorithms and software for detecting them. However there are relatively few approaches for detecting changes-in-slope in the mean of a signal plus noise model. We describe the R package, cpop, available on the Comprehensive R Archive Network (CRAN). This package implements CPOP, a dynamic programming algorithm, to find the optimal set of changes that minimises an L_0 penalised cost, with the cost being a weighted residual sum of squares. The package has extended the CPOP algorithm so it can analyse data that is unevenly spaced, allow for heterogeneous noise variance, and allows for a grid of potential change locations to be different from the locations of the data points. There is also an implementation that uses the CROPS algorithm to detect all segmentations that are optimal as you vary the L_0 penalty for adding a change across a continuous range of values.

KW - changepoints

KW - change-in-slope

KW - dynamic programming

KW - piecewise linear models

KW - structural breaks

UR - https://www.jstatsoft.org/article/view/v109i07

U2 - 10.18637/jss.v109.i07

DO - 10.18637/jss.v109.i07

M3 - Journal article

VL - 109

SP - 1

EP - 30

JO - Journal of Statistical Software

JF - Journal of Statistical Software

SN - 1548-7660

IS - 7

M1 - 7

ER -