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
McGonigle, E.T.
1eec7a96-1343-4bf5-a131-432fe50842cd
Cho, H.
09d12733-9485-4092-b519-6ac6c9cb43ee
29 June 2025
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).
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.
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2305.07581v2 (1)
- Author's Original
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asaf024
- Accepted Manuscript
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asaf024
- Version of Record
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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
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Date deposited: 14 May 2024 16:30
Last modified: 11 Sep 2025 03:35
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Author:
E.T. McGonigle
Author:
H. Cho
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