cpop: Detecting Changes in Piecewise-Linear Signals
Dublin Core
Title
cpop: Detecting Changes in Piecewise-Linear Signals
Subject
: changepoints, change-in-slope, dynamic programming, piecewise linear models,
structural breaks.
structural breaks.
Description
Changepoint detection is an important problem with a wide range of applications.
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 minimizes an L0 penalized 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 L0 penalty for adding a change across a continuous range of
values
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 minimizes an L0 penalized 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 L0 penalty for adding a change across a continuous range of
values
Creator
Paul Fearnhead
Source
https://www.jstatsoft.org/article/view/v109i07
Publisher
Lancaster Universtity
Date
May 2024
Contributor
Fajar bagus W
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Paul Fearnhead, “cpop: Detecting Changes in Piecewise-Linear Signals,” Repository Horizon University Indonesia, accessed April 4, 2025, https://repository.horizon.ac.id/items/show/8332.