Document Type

Article

Publication Date

2025

DOI

10.1111/jtsa.12833

Publication Title

Journal of Time Series Analysis

Volume

Article in Press

Pages

20 pp.

Abstract

This article combines methods from existing techniques to identify multiple changepoints in non‐Gaussian autocorrelated time series. A transformation is used to convert a Gaussian series into a non‐Gaussian series, enabling penalized likelihood methods to handle non‐Gaussian scenarios. When the marginal distribution of the data is continuous, the methods essentially reduce to the change of variables formula for probability densities. When the marginal distribution is count‐oriented, Hermite expansions and particle filtering techniques are used to quantify the scenario. Simulations demonstrating the efficacy of the methods are given and two data sets are analyzed: 1) the proportion of home runs hit by Major League Baseball batters from 1920 to 2023 and 2) a six‐dimensional series of tropical cyclone counts from the Earth's basins of generation from 1980 to 2023. In the first series, beta marginal distributions are used to describe the proportions; in the second, Poisson marginal distributions seem appropriate.

Rights

© 2025 The Authors.

This is an open access article under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Data Availability

Article states: "All data series and code are uploaded on Github.

The data that support the findings of this study are openly available in Github at https://github.com/tjfisher19/non-gaussian_changepoints."

Original Publication Citation

Lund, R., Fisher, T. J., Diawara, N., & Wehner, M. (2025). Multiple changepoint detection for non‐Gaussian time series. Journal of Time Series Analysis. Advance online publication. https://doi.org/10.1111/jtsa.12833

ORCID

0000-0002-8403-6793 (Diawara)

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