【报告摘要】 We study online prediction under distribution shift, where inputs arrive chronologically and outcomes are revealed only after prediction. In this setting, predictors must remain stable in quiet regimes yet adapt when regimes shift, and the right adaptation memory is unknown in advance. We propose MELO (Memory-hedged Exponentially Weighted Least-Squares Online aggregation), a model-agnostic method that hedges across adaptation scales: it wraps any non-anticipating base-predictor pool with exponentially weighted least-squares (EWLS) adaptation experts at multiple forgetting factors, and aggregates raw and EWLS-adapted forecasts with MLpol which is a parameter-free online aggregation rule. Under boundedness conditions, we establish deterministic oracle inequalities showing that it competes with both the best raw predictor and the best bounded, time-varying affine combinations of the base predictions, up to a path-length-dependent tracking cost and a sublinear aggregation overhead. We evaluate MELO on French national electricity-load forecasting through the COVID-19 lockdown using no regime indicators, lockdown dates, or policy covariates. MELO reduces overall RMSE by 34.7% relative to base-only MLpol and achieves lower overall RMSE than a TabICL reference supplied with an external COVID policy-response covariate. MELO requires only lightweight per-step recursive updates without model retraining.

【报告人简介】姚琦伟，1982年毕业于东南大学数学力学系，1987年于武汉大学获统计学博士学位，1989至1991年期间获德国洪堡基金会资助赴德国费莱堡大学及海德堡大学访问，1993至1995年于英国肯特大学任数学与统计学院讲师。2000至2002年在英国伦敦经济与政治科学学院统计系任副教授reader in statistics)，2002年起任该系系主任(Chalirin statistics)，2002年至今担任北大光华管理学院商务统计与经济计量系特聘教授，2010年开始担任北京大学统计科学中心科学委员会委员。现任英国伦敦经济与政治科学学院统计系教授、北京大学光华管理学院特聘教授、香港大学统计与精算学系名誉教授、英国皇家统计学会名誉会士、美国统计协会会士、数理统计学会会士。

研究领域：非线性及线性时间序列分析、非参数回归分析、时空过程分析、经验似然及bo0tstrap方法，以及统计在金融、经济等方面中的应用。