changepointGA: An R package for Fast Changepoint Detection via Genetic Algorithm

TL;DR

changepointGA is an R package that uses genetic algorithms to efficiently detect changepoints in time series data, offering faster performance and flexible modeling for biological and climate applications.

Contribution

This paper introduces changepointGA, a novel R package that applies genetic algorithms for rapid and flexible changepoint detection in large time series datasets.

Findings

Significantly faster than binary-encoded GA methods

Supports simultaneous changepoint detection and effect estimation

Effective in biological and climate data applications

Abstract

Detecting changepoints in a time series of length $N$ entails evaluating up to $2^{N-1}$ possible changepoint models, making exhaustive enumeration computationally infeasible. Genetic algorithms (GAs) provide a stochastic way to identify the structural changes: a population of candidate models evolves via selection, crossover, and mutation operators until it converges on one changepoint model that balances the goodness-of-fit with parsimony. The R package changepointGA encodes each candidate model as an integer chromosome vector and supports both the basic single-population model GA and the island model GA. Parallel computing is implemented on multi-core hardware to further accelerate computation. Users may supply custom fitness functions or genetic operators, while a user-friendly wrapper streamlines routine analyses. Extensive simulations demonstrate that our package runs significantly faster than binary-encoded GA alternatives. Additionally, this package can simultaneously locate changepoints and estimate their effects, as well as other model parameters and any integer-valued hyperparameters. Applications to array-based comparative genomic hybridization data and a century-long temperature series further highlight the package's value in biological and climate research.