Autoreservoir computing for multistep ahead prediction based on the spatiotemporal information transformation

Pei Chen, Rui Liu (), Kazuyuki Aihara and Luonan Chen ()
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Pei Chen: South China University of Technology
Rui Liu: South China University of Technology
Kazuyuki Aihara: The University of Tokyo
Luonan Chen: Chinese Academy of Sciences

Nature Communications, 2020, vol. 11, issue 1, 1-15

Abstract: Abstract We develop an auto-reservoir computing framework, Auto-Reservoir Neural Network (ARNN), to efficiently and accurately make multi-step-ahead predictions based on a short-term high-dimensional time series. Different from traditional reservoir computing whose reservoir is an external dynamical system irrelevant to the target system, ARNN directly transforms the observed high-dimensional dynamics as its reservoir, which maps the high-dimensional/spatial data to the future temporal values of a target variable based on our spatiotemporal information (STI) transformation. Thus, the multi-step prediction of the target variable is achieved in an accurate and computationally efficient manner. ARNN is successfully applied to both representative models and real-world datasets, all of which show satisfactory performance in the multi-step-ahead prediction, even when the data are perturbed by noise and when the system is time-varying. Actually, such ARNN transformation equivalently expands the sample size and thus has great potential in practical applications in artificial intelligence and machine learning.

Date: 2020
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DOI: 10.1038/s41467-020-18381-0

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