Randomly Distributed Embedding Making Short-term High-dimensional Data Predictable


Luonan Chen, Chinese Academy of Sciences


2018.09.25 14:00-15:00


601, Pao Yue-Kong Library


Future state prediction for nonlinear dynamical systems is a challenging task, particularly when only a few time-series samples for high-dimensional variables are available from real-world systems. In this work, we propose a novel model-free framework, named Randomly Distributed Embedding (RDE), to achieve accurate future state prediction based on short-term, high-dimensional data. Specifically, from the observed data of high-dimensional variables, the RDE framework randomly generates a sufficient number of lowdimensional “non-delay embeddings”, and maps each of them to a “delay embedding” which is constructed from the data of a to-bepredicted target variable. Any of these mappings can perform as a low-dimensional weak predictor for future state prediction, and all of such mappings generate a distribution of predicted future states.

This distribution actually patches all pieces of association information from various embeddings unbiasedly or biasedly into the whole dynamics of the target variable, which, after operated by appropriate estimation strategies, creates a stronger predictor for achieving prediction in a more reliable and robust form. Through applying the RDE framework to data from both representative models and realworld systems, we reveal that a high-dimension feature is no longer an obstacle but a source of information crucial to accurate prediction for short-term data even under noise deterioration.

Reference: Huanfei Ma, Siyang Leng, Wei Lin, Kazuyuki Aihara, Luonan Chen. PNAS, 2018.


LuonanChen received BS degree in the ElectricalEngineering, from Huazhong University of Science and Technology, and the M.E.and Ph.D. degrees in the electrical engineering, from Tohoku University,Sendai, Japan, in 1988 and 1991, respectively. From 1997, he was an associate professor of the Osaka Sangyo University, Osaka, Japan, and then a full Professor. Since 2010, he has been a professor and executive director at KeyLaboratory of Systems Biology, Shanghai Institutes for Biological Sciences,Chinese Academy of Sciences. He was elected as the founding president of Computational Systems Biology Society of OR China, and Chair of Technical Committee of Systems Biology at IEEE SMC Society. He serves as editor oreditorial board member for major systems biology related journals. In recentyears, he published over 300 SCI journal papers and two monographs (books) inthe area of systems biology.