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

S Kovács, P Bühlmann, H Li and A Munk

Biometrika, 2023, vol. 110, issue 1, 249-256

Abstract: SummaryWe 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.

Keywords: Binary segmentation; Breakpoint; Fast computation; High dimensionality; Minimax optimality; Multiple changepoint estimation; Narrowest-over-threshold method; Wild binary segmentation (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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