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.
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