Nonparametric data segmentation in multivariate time series via joint characteristic functions

• 成果类型: Article
• 署名作者: Mcgonigle, E. T.; Cho, H.
• 署名单位: University of Southampton; University of Bristol
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asaf024
• 发表日期: 2025
• 关键词: change-point detection; distance correlation; dependence; mosum; models; tests

摘要

Modern time series data often exhibit complex dependence and structural changes that are not easily characterized by shifts in the mean or model parameters. We propose a nonparametric data segmentation methodology for multivariate time series. By considering joint characteristic functions between the time series and its lagged values, our proposed method is able to detect changepoints in the marginal distribution, but also those in possibly nonlinear serial dependence, all without the need to prespecify the type of changes. We show the theoretical consistency of our method in estimating the total number and the locations of the changepoints, and demonstrate its good performance against a variety of changepoint scenarios. We further demonstrate its usefulness in applications to seismology and economic time series.