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arXiv AI By Yiyang Zhang, Yuli Zheng, Bin Li, Meimei Zhang, Qiang Sun

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

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The paper introduces Forecast‑Aware Residual Refinement (FARR), a method that refines long‑horizon time‑series forecasts by learning to correct predictions using endpoint evidence. FARR generates a valid forecast under normal conditions and a null forecast under a predefined null condition; the difference between these forecasts indicates how auxiliary conditioning affects the prediction. A shared residual refiner then combines this endpoint evidence with historical features to estimate the remaining error, and horizon‑wise gating controls the contribution of the predicted residual to the final output. Experiments on four long‑horizon benchmarks demonstrate that FARR achieves the best average MSE and MAE among compared methods.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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