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Downside-Controlled Online Forecast Combination under Delayed and Revised Outcomes

Kim, Minkyoung et al.
arXiv (All) · Papers
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machine-learning
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 a
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