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FARR: Forecast-Aware Residual Refinement with Matched Endpoint Evidence for Long-Horizon Time-Series Forecasting — arXiv:2611.07870

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.07870
License (as archived): CC0-1.0
Archived: 2026-11-06T14:41:45.981Z
SHA-256 of the archived text: 15171a0bf712d476126030862e58d0b30078ab0a210b6e11100daf6a254723e3
Stable URL of this page: https://metraipolis.com/documento/dc_4b25fbb439d0

How to cite

FARR: Forecast-Aware Residual Refinement with Matched Endpoint Evidence for Long-Horizon Time-Series Forecasting — arXiv:2611.07870 — arXiv (en), https://arxiv.org/abs/2611.07870 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_4b25fbb439d0 (Metraipolis archive dc_4b25fbb439d0, sha256 15171a0bf712d476126030862e58d0b30078ab0a210b6e11100daf6a254723e3)

BibTeX

@misc{dc4b25fbb439d0,
  title = {FARR: Forecast-Aware Residual Refinement with Matched Endpoint Evidence for Long-Horizon Time-Series Forecasting — arXiv:2611.07870},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.07870},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_4b25fbb439d0; sha256 15171a0bf712d476126030862e58d0b30078ab0a210b6e11100daf6a254723e3},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.07870. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Large language models use auxiliary conditioning to incorporate additional information into time-series forecasts. However, auxiliary conditioning does not consistently improve accuracy and can increase forecasting error when its influence is poorly matched to the observed temporal context. We propose Forecast-Aware Residual Refinement (FARR), which learns to correct forecasts using endpoint evidence. FARR obtains a valid forecast under the standard condition and a null forecast under a predefined null condition. Their difference reveals how conditioning changes the prediction. A shared residual refiner combines this endpoint evidence with historical features to estimate the remaining error of the valid forecast. Horizon-wise gating then controls the contribution of the predicted residual to the final output. Experiments on four long-horizon forecasting benchmarks show that FARR achieves the best dataset-average MSE and MAE among the compared methods. Code will be released at this https URL .

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2611.07870 — 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 2026-11-06T14:18:42.646Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).