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

Author

Listed:
  • Pei Chen

    (South China University of Technology)

  • Rui Liu

    (South China University of Technology)

  • Kazuyuki Aihara

    (The University of Tokyo
    The University of Tokyo)

  • Luonan Chen

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences, Chinese Academy of Sciences
    Chinese Academy of Sciences
    Zhangjiang Laboratory)

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.

Suggested Citation

  • Pei Chen & Rui Liu & Kazuyuki Aihara & Luonan Chen, 2020. "Autoreservoir computing for multistep ahead prediction based on the spatiotemporal information transformation," Nature Communications, Nature, vol. 11(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-18381-0
    DOI: 10.1038/s41467-020-18381-0
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    Cited by:

    1. Shen, Yuewen & Wen, Lihong & Shen, Chaowen, 2024. "Based on hypernetworks and multifractals: Deep distribution feature fusion for multidimensional nonstationary time series prediction," Chaos, Solitons & Fractals, Elsevier, vol. 182(C).
    2. Tao Wu & Xiangyun Gao & Feng An & Xiaotian Sun & Haizhong An & Zhen Su & Shraddha Gupta & Jianxi Gao & Jürgen Kurths, 2024. "Predicting multiple observations in complex systems through low-dimensional embeddings," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    3. Wu, Tao & Gao, Xiangyun & An, Feng & Kurths, Jürgen, 2023. "The complex dynamics of correlations within chaotic systems," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    4. Ling-Wei Kong & Gene A. Brewer & Ying-Cheng Lai, 2024. "Reservoir-computing based associative memory and itinerancy for complex dynamical attractors," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    5. Wu, Tao & Gao, Xiangyun & An, Feng & Xu, Xin & Kurths, Jürgen, 2024. "Forecasting the dynamics of correlations in complex systems," Chaos, Solitons & Fractals, Elsevier, vol. 178(C).
    6. Hajimohammadi, Zeinab & Baharifard, Fatemeh & Ghodsi, Ali & Parand, Kourosh, 2021. "Fractional Chebyshev deep neural network (FCDNN) for solving differential models," Chaos, Solitons & Fractals, Elsevier, vol. 153(P2).
    7. Sangiorgio, Matteo & Dercole, Fabio & Guariso, Giorgio, 2021. "Forecasting of noisy chaotic systems with deep neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 153(P2).

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