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TSB-SEG: A Systematic Time-Series Segmentation Benchmark — arXiv:2611.03039

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.03039
License (as archived): CC0-1.0
Archived: 2026-11-03T18:38:50.167Z
SHA-256 of the archived text: f53439e598345921e2a75e5cd201b7aa9b8c8bc77fcea3bd69d1cbb0ee1c9a5f
Stable URL of this page: https://metraipolis.com/documento/dc_625fdf42cc9e

How to cite

TSB-SEG: A Systematic Time-Series Segmentation Benchmark — arXiv:2611.03039 — arXiv (en), https://arxiv.org/abs/2611.03039 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_625fdf42cc9e (Metraipolis archive dc_625fdf42cc9e, sha256 f53439e598345921e2a75e5cd201b7aa9b8c8bc77fcea3bd69d1cbb0ee1c9a5f)

BibTeX

@misc{dc625fdf42cc9e,
  title = {TSB-SEG: A Systematic Time-Series Segmentation Benchmark — arXiv:2611.03039},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.03039},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_625fdf42cc9e; sha256 f53439e598345921e2a75e5cd201b7aa9b8c8bc77fcea3bd69d1cbb0ee1c9a5f},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.03039. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Time-series segmentation, studied as either Change Point Detection (CPD) or State Detection (SD), underpins a broad range of monitoring and diagnostic applications. However, the field suffers from a persistent divide between the statistical community (focusing on CPD) and the machine learning and data mining communities (focusing on SD). Consequently, the literature remains fragmented: evaluations typically cover narrow methodological families, datasets are often small or homogeneous, and CPD and SD are rarely evaluated together. We address these gaps with TSB-SEG, a unified benchmark comprising 592 univariate and multivariate time series from 8 heterogeneous datasets and surveying 26 algorithms that span five decades of progress. To ensure a fair comparison, we introduce a two-pool design: a scalable main pool of tractable detectors used for the aggregate ranking, and an extended-scope pool for computationally intensive methods. All methods are unified under tsseg, an open-source library to ensure full reproducibility. Our systematic evaluation yields three insights: (a) no single detector dominates across domains, and per-dataset hyperparameter tuning remains decisive for performance, (b) CPD accuracy is a useful, though imperfect, proxy for SD accuracy, and (c) data characteristics should guide method selection. We provide actionable guidance based on data properties, error tolerances, and tuning budgets, identify impactful hyperparameters, and characterize algorithm robustness to these choices.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.LG, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2611.03039 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T18:12:54.609Z dalla pagina https://arxiv.org/list/cs.LG/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).