题目：Probabilistic Forecasting for Daily Electricity Loads and Quantiles for Curve-to-Curve Regression

主讲人：伦敦政治经济学院 姚琦伟教授

主持人：统计学院 常晋源教授

时间：2022年3月22日（周二）下午16:00-17:00

地点：腾讯会议，267 667 537

报告摘要：

Probabilistic forecasting of electricity load curves is of fundamental importance for effective scheduling and decision making in the increasingly volatile and competitive energy markets. We propose a novel approach to construct probabilistic predictors for curves (PPC), which leads to a natural and new definition of quantiles in the context of curve-to-curve linear regression. There are three types of PPC: a predict set, a predictive band and a predictive quantile, and all of them are defined at a pre-specified nominal probability level. In the simulation study, the PPC achieve promising coverage probabilities under a variety of data generating mechanisms. When applying to one day ahead forecasting for the French daily electricity load curves, PPC outperform several state-of-the-art predictive methods in terms of forecasting accuracy, coverage rate and average length of the predictive bands. For example, PPC achieve up to 2.8-fold of the coverage rate with much smaller average length of the predictive bands. The predictive quantile curves provide insightful information which is highly relevant to hedging risks in electricity supply management. (Joint work with Xiuqin Xu, Ying Chen and Yannig Goude.)

主讲人简介：

姚琦伟教授，英国伦敦政治经济学院统计系教授，英国皇家统计学会会士，美国统计协会会士，数理统计学会会士，国际统计研究学会选举会员，泛华统计学会会员。主要研究领域为时间序列分析、高维时间序列建模和预测、降维和因子建模等。迄今已在AoS、Biometrika、Econometrica、JoE、JASA和JRSSB等期刊上发表学术论文80多篇，并取得EPSRC、BBSRC等英国国家基金会支持的多项研究基金项目。现在担任JRSSB的联合主编，曾担任AoS、JBES、JASA和Statistica Sinica等期刊的副主编和联合主编。