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HieraNTP: Structured Risk Representations for Time-Series Anomaly Detection via Hierarchical Next-Token Prediction — arXiv:2611.00945

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.00945
License (as archived): CC0-1.0
Archived: 2026-11-03T18:17:42.054Z
SHA-256 of the archived text: 504b754f2bf7517d4fbfbcc4e2edb75089d15ac87a2ca1a7b87bfd12732da941
Stable URL of this page: https://metraipolis.com/documento/dc_f5fa641dddf1

How to cite

HieraNTP: Structured Risk Representations for Time-Series Anomaly Detection via Hierarchical Next-Token Prediction — arXiv:2611.00945 — arXiv (en), https://arxiv.org/abs/2611.00945 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_f5fa641dddf1 (Metraipolis archive dc_f5fa641dddf1, sha256 504b754f2bf7517d4fbfbcc4e2edb75089d15ac87a2ca1a7b87bfd12732da941)

BibTeX

@misc{dcf5fa641dddf1,
  title = {HieraNTP: Structured Risk Representations for Time-Series Anomaly Detection via Hierarchical Next-Token Prediction — arXiv:2611.00945},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.00945},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_f5fa641dddf1; sha256 504b754f2bf7517d4fbfbcc4e2edb75089d15ac87a2ca1a7b87bfd12732da941},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.00945. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Time-series anomaly detection commonly converts deviations from learned temporal regularities into anomaly scores. This practice can conflate violations of temporal regularity with task-specific anomaly judgments: the same deviation may be anomalous under one task definition and benign under another. Aggregating heterogeneous deviations into a single score before task-specific adaptation can obscure the evidence needed to distinguish these cases. We introduce HieraNTP, a Hierarchical Next-Token Prediction framework that separates self-supervised predictive modeling from task-specific risk interpretation. HieraNTP pretrains a hierarchical tokenizer and a causal next-token predictor to capture temporal regularities at multiple scales. It then organizes prediction discrepancies into a structured risk representation, preserving complementary evidence across temporal scales in token, local-statistic, and observation spaces. A task-adaptive detector uses anomaly labels to select and combine this evidence for the target task. Across anomaly detection benchmarks, HieraNTP improves VUS-PR by 12.0% on TSB-AD-U and 15.5% on TSB-AD-M relative to the strongest baselines, and achieves the highest composite score on both TAB tracks, with controlled comparisons supporting the value of retaining distinct evidence. Our code is available at this https URL .

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2611.00945 — 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 2026-11-03T18:09:53.556Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).