题目：Large Segmenting Multiple Time Series by Contemporaneous Linear Transformation: PCA for Time Series

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：2021年8月10日（周二）晚上18:30-21:30

            2021年8月11日（周三）晚上18:30-21:30

            2021年8月12日（周四）晚上18:30-21:30

            2021年8月13日（周五）晚上18:30-21:30

地点：腾讯会议，652 167 503

报告摘要：

In these talks, we seek for a contemporaneous linear transformation for a p-variate time series such that the transformed series is segmented into several lower-dimensional subseries, and those subseries are uncorrelated with each other both contemporaneously and serially. The method may be viewed as an extension of principal component analysis (PCA) for multiple time series. Technically it also boils down to an eigen analysis for a positive definite matrix. When p is large, an additional step is required to perform a permutation in terms of either maximum cross-correlations or FDR based on multiple tests. The asymptotic theory is established for both fixed p and diverging p when the sample size n tends to infinity. Numerical experiments with both simulated and real datasets indicate that the proposed method is an effective initial step in analysing multiple time series data, which leads to substantial dimension-reduction in modelling and forecasting high-dimensional linear dynamical structures. The method can also be adapted to segment multiple volatility processes.

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90余篇，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。