cpop: Detecting changes in piecewise-linear signals

TL;DR

The paper introduces the R package cpop, which implements an advanced dynamic programming algorithm for detecting change-in-slope points in piecewise-linear signals, accommodating uneven spacing, heteroscedastic noise, and variable change locations.

Contribution

It presents a novel implementation of the CPOP algorithm in R, extending its capabilities to handle more complex data scenarios and providing tools for comprehensive change detection analysis.

Findings

Efficient detection of change-in-slope in noisy signals.

Extended CPOP to handle unevenly spaced data and heteroscedastic noise.

Provides the CROPS algorithm for exploring multiple segmentations.

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.