tidychangepoint: A Unified Framework for Analyzing Changepoint Detection in Univariate Time Series

TL;DR

tidychangepoint is an R package that unifies various changepoint detection methods for univariate time series, enabling easy comparison and analysis within a consistent framework.

Contribution

It introduces a common data structure and interface for multiple changepoint detection algorithms, simplifying comparison and integration of diverse methods.

Findings

Supports both deterministic and randomized algorithms

Provides a unified, tidyverse-compatible framework

Facilitates comparative analysis of changepoint methods

Abstract

We present tidychangepoint, a new R package for changepoint detection analysis. Most R packages for segmenting univariate time series focus on providing one or two algorithms for changepoint detection that work with a small set of models and penalized objective functions, and all of them return a custom, nonstandard object type. This makes comparing results across various algorithms, models, and penalized objective functions unnecessarily difficult. tidychangepoint solves this problem by wrapping functions from a variety of existing packages and storing the results in a common S3 class called tidycpt. The package then provides functionality for easily extracting comparable numeric or graphical information from a tidycpt object, all in a tidyverse-compliant framework. tidychangepoint is versatile: it supports both deterministic algorithms like PELT (from changepoint), and also flexible, randomized, genetic algorithms (via GA) that -- via new functionality built into tidychangepoint -- can be used with any compliant model-fitting function and any penalized objective function. By bringing all of these disparate tools together in a cohesive fashion, tidychangepoint facilitates comparative analysis of changepoint detection algorithms and models.