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

Summary 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...

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Bibliographic Details
Published in:Biometrika Vol. 110; no. 1; pp. 249 - 256
Main Authors: Kovács, S, Bühlmann, P, Li, H, Munk, A
Format: Journal Article
Language:English
Published: Oxford University Press 01.03.2023
Subjects:
Fast computation
Breakpoint
Wild binary segmentation
Multiple changepoint estimation
High dimensionality
Binary segmentation
Minimax optimality
Narrowest-over-threshold method
ISSN:0006-3444, 1464-3510
Online Access:Get full text
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Summary:Summary 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.
ISSN:0006-3444
1464-3510
DOI:10.1093/biomet/asac052