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Bayesian Reconciliation of Overlapping Multi-Step Forecasts: A Post-Processing Framework for Cross-Origin Coherence — arXiv:2610.05340

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.05340
License (as archived): CC0-1.0
Archived: 2026-10-06T09:36:34.237Z
SHA-256 of the archived text: bf16ec08cf778fd8b057fedfd58464501bac628c903b41d9c934005c102163a0
Stable URL of this page: https://metraipolis.com/documento/dc_d449ecbf8ec0

How to cite

Bayesian Reconciliation of Overlapping Multi-Step Forecasts: A Post-Processing Framework for Cross-Origin Coherence — arXiv:2610.05340 — arXiv (en), https://arxiv.org/abs/2610.05340 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_d449ecbf8ec0 (Metraipolis archive dc_d449ecbf8ec0, sha256 bf16ec08cf778fd8b057fedfd58464501bac628c903b41d9c934005c102163a0)

BibTeX

@misc{dcd449ecbf8ec0,
  title = {Bayesian Reconciliation of Overlapping Multi-Step Forecasts: A Post-Processing Framework for Cross-Origin Coherence — arXiv:2610.05340},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.05340},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_d449ecbf8ec0; sha256 bf16ec08cf778fd8b057fedfd58464501bac628c903b41d9c934005c102163a0},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.05340. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Multi-step forecasting systems, including modern artificial intelligence (AI) models, often issue overlapping predictions for the same future time from different forecast origins. These predictions can disagree even when produced by the same model, a phenomenon we call cross-origin incoherence. We propose a Bayesian reconciliation framework that treats overlapping forecasts as noisy and potentially biased measurements of a common latent trajectory. The method operates as a model agnostic post-processing layer, requiring no access to the forecasting models architecture, parameters, or training data. A Bayesian state-space model jointly reconciles forecasts targeting the same future time while accounting for horizon-specific bias and uncertainty. Controlled simulations demonstrate substantial reductions in cross-origin incoherence and accurate latent trajectory recovery under the assumed data generating process. An empirical application to daily S and P 500 volatility forecasts reduces 90% prediction interval coverage from 94.8% to 88.3%, closer to nominal coverage, but increases point forecast error. An additional LSTM experiment demonstrates applicability to modern neural forecasting systems.

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv stat.ME, nuovi lavori del giorno).
Categorie arXiv: Methodology (stat.ME).
Pagina del lavoro: https://arxiv.org/abs/2610.05340 — 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 2026-10-06T08:24:29.087Z dalla pagina https://arxiv.org/list/stat.ME/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).