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Generalist Representation, Specialist Detection: TS-Router for Time-Series Anomaly Detection — arXiv:2610.00978

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.00978
License (as archived): CC0-1.0
Archived: 2026-10-06T12:12:14.328Z
SHA-256 of the archived text: 002727ad970bbf04b64d4d2884d9c65dc0a1d757f706d7619f2bf8349256af7d
Stable URL of this page: https://metraipolis.com/documento/dc_ef05cd32699b

How to cite

Generalist Representation, Specialist Detection: TS-Router for Time-Series Anomaly Detection — arXiv:2610.00978 — arXiv (en), https://arxiv.org/abs/2610.00978 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ef05cd32699b (Metraipolis archive dc_ef05cd32699b, sha256 002727ad970bbf04b64d4d2884d9c65dc0a1d757f706d7619f2bf8349256af7d)

BibTeX

@misc{dcef05cd32699b,
  title = {Generalist Representation, Specialist Detection: TS-Router for Time-Series Anomaly Detection — arXiv:2610.00978},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.00978},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ef05cd32699b; sha256 002727ad970bbf04b64d4d2884d9c65dc0a1d757f706d7619f2bf8349256af7d},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.00978. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Time-series anomaly detection (TSAD) is difficult to generalize across datasets because heterogeneous temporal dynamics imply different notions of normality and favor different detection criteria. While time-series foundation models provide transferable representations, coupling them with a fixed anomaly-scoring mechanism can overlook this variation. This motivates a different perspective on foundation-model-based TSAD: using foundation models to coordinate specialized anomaly criteria rather than directly imposing a universal one. Based on this view, we propose \textbf{TS-Router}, a generalist-representation, specialist-detection framework that estimates the relative competence of heterogeneous anomaly detectors from pretrained temporal representations and selects suitable specialists for each target series. To avoid relying on specialist-performance labels from real tasks, we derive soft competence supervision from specialists' relative performance on labeled simulated tasks. At deployment, routing requires no target anomaly labels, and only the selected specialists are fitted unsupervisedly on the target series. We bound Top-\(k\) set-competence regret under representation coverage and conditional competence stability. Across 16 real-world benchmarks and four complementary evaluation metrics, TS-Router achieves the best overall average rank. Controlled ablations with multiple frozen TSFM encoders further support the use of pretrained representations for competence estimation and adaptive specialist selection. The code is available at this https URL .

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-10-02 in cs.AI).
Categorie arXiv: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2610.00978 — 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 2026-10-06T10:21:28.140Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-10-02?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).