Statistical Random Forests Modelling for Nonlinear Time Series
Prof. Zudi LU
Professor of Biostatistics
City University of Hong Kong
Date: 28 November 2025 (Friday)
Time: 16:00-17:30
Venue: E22-2014
Host: Prof. Degui LI, Distinguished Professor in Business Economics
Abstract
Random forest is a popular machine learning method in many applications. It heavily depends on integrating the trees constructed by the technique of bootstrap resampling of the original data. However, for the time series, it is often challenging to do this owing to the unknown underlying dependence of the data. This paper explores the development of an efficient Random Forests by Random Weights (RF-RW) methodology tailored for time series modeling. Addressing limitations of traditional random forests, the proposed approach handles dependent time series data, moving beyond the assumption of independent and identically distributed (i.i.d.) observations. The project combines theoretical advancements with practical evaluations, including simulation studies and applications to UK COVID-19 daily case modeling. Results demonstrate the superior performance of RF-RW compared to existing methods like original random forests and random forests with traditional time series resampling as well as other machine learning methods including SVMs, and LSTMs. This is a joint work of Shihao Zhang and Chao Zheng.
Speaker
Prof. Zudi LU is Professor at Department of Biostatistics of City University of Hong Kong. His research interests include: Statistical inference & computation for dynamic modeling with statistical (deep) learning, (causal) reasoning and prediction of nonlinear spatial/temporal (tensor) big data; Nonlinear time series modeling and financial statistics/econometrics; Medical/health Statistics; Applied temporal/spatial modeling for risk analysis; Non-parametric/semi-parametric and machine learning methods. Prof.Lu was a recipient of the Australian Research Council Future Fellowship Award and the Marie Curie Actions (People) Career Integration Grant Award. He was also a joint recipient of a Key Project fund award from the National Natural Science Foundation of China in 2000. He is an elected member of the International Statistical Institute. He is currently an Associate Editor with the international journals such as Journal of Time Series Analysis.
All are welcome!
