学术报告

报告题目: 基于动力学重构的因果探测与时间序列预测

报 告 人： 林伟 教授 （复旦大学）

报告时间：2019年5月10日（星期五）15：30-16：30

报告地点：星际电子在线学术报告厅（25教14楼）

参加人员：教师、研究生、本科生

报告简介：In this talk, I will introduce two model-free frameworks of dynamical time series analytics. One framework is to detect the causation interactions among a large group of dynamical variables, which probably recovers a network hidden in a real-world system we are concerned. The second framework is to make a forecast or future prediction based only on short-term and high-dimensional time series, which is usually believed to be a challenging task. Both frameworks use the advantages of Taken's embedding techniques, which reveals that utilization of dynamical system theory is more likely to exploit useful information from time series not only from the models but also from the real-world systems.

报告人人简介：Dr. Wei Lin received the Ph.D. degrees in applied mathematics from Fudan University, Shanghai, China, in January, 2003 with specialization in dynamical systems, bifurcation and chaos theory. Now, he is a Professor in applied mathematics of Fudan University, China. Currently, he is serving as the Vice Dean of the Institute of Science and Technology for Brain-Inspired Intelligence and as the Director of the Centre for Computational Systems Biology, Fudan University, China. His current research interests include bifurcation and chaos theory, stability and oscillations in hybrid systems, stochastic systems and complex networks, data assimilation, causality analysis, and their applications to systems biology and artificial intelligence. He has his recent works published in the prestigious journals including PNAS, PRL, IEEE TAC, and CHAOS. Dr. Lin is currently the Vice Chair of the Shanghai Society of Nonlinear Sciences, a Board Member of the International Physics and Control Society, an AE of the International Journal of Bifurcation and Chaos, and a member of Editorial Advisory Board of CHAOS. He received the Excellent Young Scholar Fund from NSFC in 2013, and becomes a Highly Cited Chinese Researcher in General Engineering according to Elsevier.