講座主題：On the modelling and prediction of high-dimensional functional time series

主講嘉賓：常晉源，西南财經大學

講座時間：2023年05月31日14:00-16:00

講座地點：bevictor伟德官网沙河校區學院11号樓308

嘉賓簡介：常晉源，西南财經大學光華特聘教授、中國科學院數學與系統科學研究院研究員、博士生導師、數據科學與商業智能聯合實驗室執行主任、國家傑出青年科學基金獲得者、四川省特聘專家、四川省統計專家咨詢委員會委員。常晉源老師的研究興趣主要集中在“超高維數據分析”和“高頻金融數據分析”兩個領域。常晉源老師的研究論文發表于Journal of Econometrics，Biometrika，Biometrics，The Annals of Statistics，Journal of the American Statistical Association，Journal of Business & Economic Statistics等統計學和計量學國際頂尖雜志。常晉源老師曾擔任Journal of the Royal Statistical Society SeriesB副主編，現擔任Journal of the AmericanStatistical Association、Journal of Business & Economic Statistics以及Statistica Sinica的副主編。

内容摘要：We propose a two-step procedure to model and predict high-dimensional functional time series, where the number of function-valued time series p is large in relation to the length of time series n. Our first step performs an eigenanalysis of a positive definite matrix, which leads to a one-to-one linear transformation for the original high-dimensional functional time series, and the transformed series can be segmented into several groups such that any two series from any two different groups are uncorrelatedboth contemporaneously and serially. Consequently in our second step those groups are handled separately without the information loss on the overall linear dynamic structure. The second step is devoted to establishing a finite-dimensional dynamical structure for all the functional time series within each group. Furthermore the finite-dimensional structure is represented by that of a vector time series. Modelling and forecasting for the original high-dimensional functional time series are realized via those for the vector time series in all the groups. We investigate the theoretical properties of our proposed methods, and illustrate the finite-sample performance through both extensive simulations and three real datasets.

撰稿人：學術活動項目組

審稿人：郭冬梅