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arXiv · 2407.14369

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

Benjamin S. Baumer·Biviana Marcela Suarez Sierra
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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.

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BibTeXRIS

Benjamin S. Baumer, Biviana Marcela Suarez Sierra. 2024-07-19. tidychangepoint: A Unified Framework for Analyzing Changepoint Detection in Univariate Time Series. https://arxiv.org/abs/2407.14369

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