延迟与修正结果下的下行受控在线预测组合
Downside-Controlled Online Forecast Combination under Delayed and Revised Outcomes
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中文总结 AI 辅助
本研究提出一种下行受控的在线预测组合方法，结合冻结预测器与静态及在线修正器，在延迟和修正结果下实现不劣于初始预测的性能，并在多个基准和负荷预测中验证了其有效性。
中文摘要 AI 辅助
事后修正调整无法重新训练的预测器（如基础模型），但在误差稳定处拟合的修正可能在误差变化处造成损害。我们追求下行控制：即不比初始预测差太多。我们在单纯形上组合冻结预测器、静态修正器和在线修正器，仅使用在预测期限后成熟的损失。在七个基准和四个基础模型（其中两个为基础模型）上，主预测期限的28对比较中最差恶化率为0.15%，收益最高达11.5%。在七个欧洲竞价区的日前负荷预测中，该方法在所有七个区域降低了平均均方误差，而单一修正器在已发布预测最准确的区域将平均均方误差提高了高达102%。关于专家速度、流长度和结果对齐的三个经验条件，每个条件均由一个已记录失败案例确定，界定了其适用范围。从临时结果学习改善了四个区域相对于结算结果的表现；从结算结果学习则恢复了全部七个区域。
英文摘要
Post-hoc correction adjusts a forecaster that cannot be retrained, such as a foundation model, but a correction fitted where errors are stable can hurt where they shift. We aim for downside control: not much worse than the starting forecast. We combine the frozen forecaster, a static corrector and an online corrector on the simplex, using only losses that mature after the horizon. Across seven benchmarks and four base models, two of them foundation models, the worst deterioration over 28 pairs at the main horizon is 0.15% and gains reach 11.5%. On day-ahead load for seven European bidding zones it lowers mean MSE in all seven zones, while single correctors raise mean MSE by up to 102% where the published forecast is most accurate. Three empirical conditions on expert speed, stream length and outcome alignment, each fixed by a documented failure, delimit its scope. Learning from the provisional outcome improves four zones on the settled one; learning on the settled outcome restores all seven.
发表机构
- Yonsei University（延世大学）
机构由 AI 辅助整理，请以论文原文为准。
