The University of Southampton
University of Southampton Institutional Repository

Nonparametric data segmentation in multivariate time series via joint characteristic functions

Nonparametric data segmentation in multivariate time series via joint characteristic functions
Nonparametric data segmentation in multivariate time series via joint characteristic functions
Modern time series data often exhibit complex dependence and structural changes which are not easily characterised by shifts in the mean or model parameters. We propose a nonparametric data segmentation methodology for multivariate time series termed NP-MOJO. By considering joint characteristic functions between the time series and its lagged values, NP-MOJO is able to detect change points in the marginal distribution, but also those in possibly non-linear serial dependence, all without the need to pre-specify the type of changes. We show the theoretical consistency of NP-MOJO in estimating the total number and the locations of the change points, and demonstrate the good performance of NP-MOJO against a variety of change point scenarios. We further demonstrate its usefulness in applications to seismology and economic time series.
Changepoint detection, Joint characteristic function, Moving sum, Multivariate time series, Nonparametric data
0006-3444
McGonigle, E.T.
1eec7a96-1343-4bf5-a131-432fe50842cd
Cho, H.
09d12733-9485-4092-b519-6ac6c9cb43ee
McGonigle, E.T.
1eec7a96-1343-4bf5-a131-432fe50842cd
Cho, H.
09d12733-9485-4092-b519-6ac6c9cb43ee

McGonigle, E.T. and Cho, H. (2025) Nonparametric data segmentation in multivariate time series via joint characteristic functions. Biometrika, 112 (2), [asaf024]. (doi:10.1093/biomet/asaf024).

Record type: Article

Abstract

Modern time series data often exhibit complex dependence and structural changes which are not easily characterised by shifts in the mean or model parameters. We propose a nonparametric data segmentation methodology for multivariate time series termed NP-MOJO. By considering joint characteristic functions between the time series and its lagged values, NP-MOJO is able to detect change points in the marginal distribution, but also those in possibly non-linear serial dependence, all without the need to pre-specify the type of changes. We show the theoretical consistency of NP-MOJO in estimating the total number and the locations of the change points, and demonstrate the good performance of NP-MOJO against a variety of change point scenarios. We further demonstrate its usefulness in applications to seismology and economic time series.

Text
2305.07581v2 (1) - Author's Original
Available under License Creative Commons Attribution.
Download (868kB)
Text
asaf024 - Accepted Manuscript
Available under License Creative Commons Attribution.
Download (1MB)
Text
asaf024 - Version of Record
Available under License Creative Commons Attribution.
Download (594kB)

More information

Submitted date: 12 May 2023
Accepted/In Press date: 17 March 2025
e-pub ahead of print date: 1 April 2025
Published date: 29 June 2025
Keywords: Changepoint detection, Joint characteristic function, Moving sum, Multivariate time series, Nonparametric data

Identifiers

Local EPrints ID: 490040
URI: http://eprints.soton.ac.uk/id/eprint/490040
ISSN: 0006-3444
PURE UUID: 8917be27-ee24-4d67-9e38-b463e4d71b56
ORCID for E.T. McGonigle: ORCID iD orcid.org/0000-0003-0902-0035

Catalogue record

Date deposited: 14 May 2024 16:30
Last modified: 11 Sep 2025 03:35

Export record

Altmetrics

Contributors

Author: E.T. McGonigle ORCID iD
Author: H. Cho

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×