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

Authors: Yiyang Zhang , Yuli Zheng , Bin Li , Meimei Zhang , Qiang Sun

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Abstract: 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 .

Comments:

10 pages, 3 figures, 4 tables

Subjects:

Artificial Intelligence (cs.AI)

Cite as:

arXiv:2611.07870 [cs.AI]

(or

arXiv:2611.07870v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2611.07870

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arXiv-issued DOI via DataCite (pending registration)

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From: Zhangyiyang Zhang Yiyang [ view email ]

[v1]

Thu, 5 Nov 2026 11:45:32 UTC (1,364 KB)

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