Segmenting Time Series via Self-Normalization

• Activity Number: 356 - Recent Advances in Change-Point Analysis for Business and Economics Data
• Type: Invited
• Date/Time: Thursday, August 12, 2021 : 12:00 PM to 1:50 PM
• Sponsor: Business and Economic Statistics Section
• Author(s): Zifeng Zhao* and Feiyu Jiang and Xiaofeng Shao
• Companies: University of Notre Dame and Tsinghua University and University of Illinois at Urbana-Champaign
• Keywords: Binary segmentation; Change-point detection; Studentization; Long-run variance; Temporal dependence

Abstract

We propose a novel and unified framework for change-point estimation in multivariate time series. The proposed method is fully nonparametric, tuning-free and robust to temporal dependence. Moreover, it treats change-point detection for a broad class of parameters (such as mean, variance, correlation and quantile) in a unified fashion. At the core of our method, we couple the self-normalization (SN) based tests with a novel nested local-window segmentation algorithm, which seems new in the growing literature of change-point analysis. Due to the presence of an inconsistent long-run variance estimator in the SN test, non-standard theoretical arguments are further developed to derive the consistency and convergence rate of the SN-based change-point detection method. Extensive numerical experiments and relevant real data analysis are conducted to illustrate the effectiveness and broad applicability of our proposed method in comparison with state-of-the-art approaches in the literature.