dorsal/arxiv
View SchemaInference for Multiple Change-points in Piecewise Locally Stationary Time Series
| Authors | Wai Leong Ng, Xinyi Tang, Mun Lau Cheung, Jiacheng Gao, Chun Yip Yau, Holger Dette |
|---|---|
| Categories | |
| ArXiv ID | 2601.07400vv2 |
| URL | https://arxiv.org/abs/2601.07400 |
| License | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
Abstract
Change-point detection and locally stationary time series modeling are two major approaches for the analysis of non-stationary data. The former aims to identify stationary phases by detecting abrupt changes in the dynamics of a time series model, while the latter employs (locally) time-varying models to describe smooth changes in dependence structure of a time series. However, in some applications, abrupt and smooth changes can co-exist, and neither of the two approaches alone can model the data adequately. In this paper, we propose a novel likelihood-based procedure for the inference of multiple change-points in locally stationary time series. In contrast to traditional change-point analysis where an abrupt change occurs in a real-valued parameter, a change in locally stationary time series occurs in a parameter curve, and can be classified as a jump or a kink depending on whether the curve is discontinuous or not. We show that the proposed method can consistently estimate the number, locations, and the types of change-points. Two different asymptotic distributions corresponding respectively to jump and kink estimators are also established. Extensive simulation studies and a real data application to financial time series are provided.
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"abstract": "Change-point detection and locally stationary time series modeling are two major approaches for the analysis of non-stationary data. The former aims to identify stationary phases by detecting abrupt changes in the dynamics of a time series model, while the latter employs (locally) time-varying models to describe smooth changes in dependence structure of a time series. However, in some applications, abrupt and smooth changes can co-exist, and neither of the two approaches alone can model the data adequately. In this paper, we propose a novel likelihood-based procedure for the inference of multiple change-points in locally stationary time series. In contrast to traditional change-point analysis where an abrupt change occurs in a real-valued parameter, a change in locally stationary time series occurs in a parameter curve, and can be classified as a jump or a kink depending on whether the curve is discontinuous or not. We show that the proposed method can consistently estimate the number, locations, and the types of change-points. Two different asymptotic distributions corresponding respectively to jump and kink estimators are also established. Extensive simulation studies and a real data application to financial time series are provided.",
"arxiv_id": "2601.07400",
"authors": [
"Wai Leong Ng",
"Xinyi Tang",
"Mun Lau Cheung",
"Jiacheng Gao",
"Chun Yip Yau",
"Holger Dette"
],
"categories": [
"stat.ME",
"math.ST",
"stat.TH"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"title": "Inference for Multiple Change-points in Piecewise Locally Stationary Time Series",
"url": "https://arxiv.org/abs/2601.07400",
"version": "v2"
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