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Dynamics evolution prediction from time series data with recurrent neural networks in a complex system

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  • Yixin Liu

    (Beijing University of Posts and Telecommunications, International School, Xitucheng Road No. 10, Beijing 100876, P. R. China)

Abstract

Time series data can be used to predict the dynamical behaviors without knowing equation model of a system. In this study, long-short term memory (LSTM) neural network is implemented to construct a complex dynamical system from data series. The network is trained through minimizing the loss function to obtain the optimal weight matrices of LSTM cells. We find that the LSTM network can well †learn†the information of the complex system. The data series generated from periodic orbits of a nonlinear system can be exactly predicted by comparing the output of neural networks with the real complex system. For the chaotic data series, the time evolution of trajectories could exactly match the actual system in the short-term data. Moreover, the long-term ergodic behavior of the complex system remains in our prediction, although such chaotic data series are quite sensitive to the initial conditions and the ensuing increase in uncertainty.

Suggested Citation

  • Yixin Liu, 2023. "Dynamics evolution prediction from time series data with recurrent neural networks in a complex system," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 34(08), pages 1-11, August.
    • Handle: RePEc:wsi:ijmpcx:v:34:y:2023:i:08:n:s0129183123500997
    DOI: 10.1142/S0129183123500997
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