讲座题目：Quantiles for Curve-to-Curve Regression and Probabilistic Forecasting for Daily Electricity Load Curves

主讲人：姚琦伟教授  英国伦敦经济与政治科学学院

讲座时间：2020年9月8日（周二）下午16：00-18：00

参与方式： 本场报告将通过腾讯会议举办，

会议 ID：418 188 690

会议直播:https://meeting.tencent.com/l/vST3VwyiD4TL

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主讲人简介：

   姚琦伟教授是国际知名的统计学家，一直从事统计学的教学和科研工作，主要研究领域为：时间序列分析、时空过程分析、金融计量经济学。他在非线性和高维时间序列方面的研究国际领先。姚琦伟教授迄今已发表学术论文80多篇， 并获得EPSRC, BBSRC等英国国家基金会支持的多项研究基金项目。其专著《非线性时间序列：非参数及参数方法》（与范剑青合著）于2003年由Springer 出版，《计量金融简要》（与范剑青合著）于2017年由剑桥出版社出版。姚琦伟教授已担任包括Annals of Statistics，Journal of the American Statistics Association, Journal of the Royal Statistical Society (Series B) 等多个顶级杂志副主编，并曾任 Statistica Sinica的联合主编。姚琦伟教授还曾为巴克莱银行，法国电力公司以及Winton资本等多家企业提供咨询。

讲座摘要：

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)