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arXiv Machine Learning By Minkyoung Kim, Hyunjung Byun, Yohan Lee, Beakcheol Jang

Downside-Controlled Online Forecast Combination under Delayed and Revised Outcomes

Read the original on arXiv Machine Learning →

The paper proposes a method for controlling downside risk when adjusting forecasts from frozen models, such as foundation models, by combining a static corrector and an online corrector on the simplex. Using only post‑horizon losses, the approach achieves minimal deterioration (0.15%) and up to 11.5% gains across 28 forecast pairs, and consistently reduces mean MSE in day‑ahead load forecasts for seven European bidding zones. The method’s applicability is bounded by three empirical conditions related to expert speed, stream length, and outcome alignment.

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 Machine Learning.

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