A0943
Title: Factor modelling for matrix-variate functional time series in high dimensions
Authors: Dong Li - Tsinghua University (China)
Xinghao Qiao - London Schhol of Economics (United Kingdom)
Zihan Wang - Tsinghua University (China) [presenting]
Abstract: Nowadays, the analysis of interconnected systems is crucial across various fields, including transportation and social networks. To address this challenge, the aim is to introduce factor modelling for a new data type known as matrix-variate functional time series, which competes with existing factor modelling for tensor-time series by treating intraday observations as random functions instead of random vectors. Theoretical results on the consistency of the estimated quantities under mild conditions have been provided, and its finite-sample performances have been illustrated through extensive simulations under both fully and partially observed scenarios. Real data examples about dynamic transportation networks have been exercised to demonstrate the advantages of our proposed method in terms of flexibility, interpretability and forecasting performance compared to the tensor-based method.
