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Conformal Coverage of Time Series: Validity and Inference — arXiv:2609.33868

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.33868
License (as archived): CC0-1.0
Archived: 2026-10-07T23:02:49.611Z
SHA-256 of the archived text: a87d1874ca1d8c94a0e80c747976ddd28cf8bbd33e49c96e5c2ac4cf79a385a6
Stable URL of this page: https://metraipolis.com/documento/dc_2dcfab9fcd2d

How to cite

Conformal Coverage of Time Series: Validity and Inference — arXiv:2609.33868 — arXiv (en), https://arxiv.org/abs/2609.33868 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_2dcfab9fcd2d (Metraipolis archive dc_2dcfab9fcd2d, sha256 a87d1874ca1d8c94a0e80c747976ddd28cf8bbd33e49c96e5c2ac4cf79a385a6)

BibTeX

@misc{dc2dcfab9fcd2d,
  title = {Conformal Coverage of Time Series: Validity and Inference — arXiv:2609.33868},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.33868},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_2dcfab9fcd2d; sha256 a87d1874ca1d8c94a0e80c747976ddd28cf8bbd33e49c96e5c2ac4cf79a385a6},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.33868. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Conformal prediction provides marginal coverage guarantees, yet practitioners may wonder if the observed coverage is truly abnormal or consistent with sampling variation. Inference for realized coverage has received comparatively little attention, especially for time series. We study split conformal prediction with adjacent calibration and test sets of temporally dependent data. Using the functional dependence measure, we derive non-asymptotic bounds on marginal coverage error without mixing assumptions, which can be difficult to verify and may fail even for simple short-memory models. We establish a Bahadur representation to derive, to our knowledge, the first central limit theorem for realized coverage of split conformal prediction under temporal dependence. A consistent block-based estimator of the standard error yields an asymptotically justified test. We further study long-memory time series, which remain understudied in conformal prediction. For Gaussian linear processes, we show how very strong temporal dependence can lead to a non-Gaussian limiting law of realized coverage and establish block-sampling inference with an estimated normalization. The resulting theory explains how temporal dependence changes coverage uncertainty.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-29 in stat.ML).
Categorie arXiv: Statistics Theory (math.ST) ; Methodology (stat.ME); Machine Learning (stat.ML).
Pagina del lavoro: https://arxiv.org/abs/2609.33868 — 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 2026-10-07T22:41:13.208Z dalla pagina https://arxiv.org/catchup/stat.ML/2026-09-29?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).