  Inspired by the demands of real-time climate and weather forecasting, we
develop optimistic online learning algorithms that require no parameter tuning
and have optimal regret guarantees under delayed feedback. Our algorithms --
DORM, DORM+, and AdaHedgeD -- arise from a novel reduction of delayed online
learning to optimistic online learning that reveals how optimistic hints can
mitigate the regret penalty caused by delay. We pair this delay-as-optimism
perspective with a new analysis of optimistic learning that exposes its
robustness to hinting errors and a new meta-algorithm for learning effective
hinting strategies in the presence of delay. We conclude by benchmarking our
algorithms on four subseasonal climate forecasting tasks, demonstrating low
regret relative to state-of-the-art forecasting models.
