Title:On the modelling and prediction of high-dimensional functional time series

Speakers:JinyuanChang(Southwest University of Finance and Economics)

Time:May31, 2023 (Wednesday),14:00-16:00

Venue: Meeting Room 308, College Building 11

Abstract: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.

Writor:Academic Activity Project Team

Reviewer:Dongmei Guo