Seeded binary segmentation: a general methodology for fast and optimal changepoint detection

• 成果类型: Article
• 署名作者: Kovacs, S.; Buehlmann, P.; Li, H.; Munk, A.
• 署名单位: Swiss Federal Institutes of Technology Domain; ETH Zurich; University of Gottingen
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asac052
• 发表日期: 2023
• 页码: 249256
• 关键词: criterion; scan

摘要

We propose seeded binary segmentation for large-scale changepoint detection problems. We construct a deterministic set of background intervals, called seeded intervals, in which single changepoint candidates are searched for. The final selection of changepoints based on these candidates can be done in various ways, adapted to the problem at hand. The method is thus easy to adapt to many changepoint problems, ranging from univariate to high dimensional. Compared to recently popular random background intervals, seeded intervals lead to reproducibility and much faster computations. For the univariate Gaussian change in mean set-up, the methodology is shown to be asymptotically minimax optimal when paired with appropriate selection criteria. We demonstrate near-linear runtimes and competitive finite sample estimation performance. Furthermore, we illustrate the versatility of our method in high-dimensional settings.