Prof. Wei Lin's joint paper with Kazuyuki Aihara, Luonan Chen et al. is published in Proc. Natl. Acad. Sci.


The paper by Wei Lin and his collaborators Luonan Chen, Kazuyuki Aihara and Huanfei Ma, Siyang Leng, entitled "Randomly distributed embedding making short-term high-dimensional data predictable",  has been published in the journal "Proc. Natl. Acad. Sci." in 2018.

Making accurate forecast or prediction is a challenging task in the big data era, in particular for those datasets involv- ing high-dimensional variables but short-term time series points, and these datasets are omnipresent in many fields. This paper proposes a model-free framework, named as "randomly distributed embedding" (RDE), to accurately predict future dynamics based on such short-term but high-dimensional data. The RDE framework creates the distribution information from the interactions among high-dimensional variables to compensate for the lack of time points in real applications. Instead of roughly predicting a single trial of future values, this framework achieves the accurate prediction by using the distribution information, and indeed shows great potential of applications in a broad range of science and engineering fields.

This paper's corresponding authors are Kazuyuki Aihara, Wei Lin and Luonan Chen. The first author of this paper is Huanfei Ma.

220 Handan Rd., Yangpu District, Shanghai ( 200433 ) | Operator:+86 21 65642222

Copyright © 2016 FUDAN University. All Rights Reserved