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Chaotic time series prediction based on multi-scale attention in a multi-agent environment

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  • Miao, Hua
  • Zhu, Wei
  • Dan, Yuanhong
  • Yu, Nanxiang

Abstract

A new problem at the intersection of multi-agent systems, chaotic time series prediction, and flow map learning is formulated in this paper. The problem involves agents collaborating to track moving targets in chaotic dynamic systems by communicating. Inspired by the multi-scale hierarchical time-stepper (HiTS), a novel Distributed Prediction Network based on Multi-scale Attention (DPNMA) is proposed to fuse predictions from agents at different scales through an enhanced self-attention mechanism. The experimental evaluation demonstrates that DPNMA effectively mitigates cumulative errors and enhances the accuracy and robustness of the predictions, which has important implications for the scenarios where the agents have heterogeneous and constrained capabilities.

Suggested Citation

  • Miao, Hua & Zhu, Wei & Dan, Yuanhong & Yu, Nanxiang, 2024. "Chaotic time series prediction based on multi-scale attention in a multi-agent environment," Chaos, Solitons & Fractals, Elsevier, vol. 183(C).
    • Handle: RePEc:eee:chsofr:v:183:y:2024:i:c:s0960077924004272
    DOI: 10.1016/j.chaos.2024.114875
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    References listed on IDEAS

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    1. Wenjie Lu & Jiazheng Li & Yifan Li & Aijun Sun & Jingyang Wang, 2020. "A CNN-LSTM-Based Model to Forecast Stock Prices," Complexity, Hindawi, vol. 2020, pages 1-10, November.
    2. Fu, Ke & Li, He & Deng, Pengfei, 2022. "Chaotic time series prediction using DTIGNet based on improved temporal-inception and GRU," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).
    3. Sun, Ying & Zhang, Luying & Yao, Minghui, 2023. "Chaotic time series prediction of nonlinear systems based on various neural network models," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).
    4. Uribarri, Gonzalo & Mindlin, Gabriel B., 2022. "Dynamical time series embeddings in recurrent neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 154(C).
    5. Sangiorgio, Matteo & Dercole, Fabio & Guariso, Giorgio, 2021. "Forecasting of noisy chaotic systems with deep neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 153(P2).
    6. Qinghai Li & Rui-Chang Lin, 2016. "A New Approach for Chaotic Time Series Prediction Using Recurrent Neural Network," Mathematical Problems in Engineering, Hindawi, vol. 2016, pages 1-9, December.
    7. Bethany Lusch & J. Nathan Kutz & Steven L. Brunton, 2018. "Deep learning for universal linear embeddings of nonlinear dynamics," Nature Communications, Nature, vol. 9(1), pages 1-10, December.
    8. Lilian Huang & Zefeng Zhang & Jianhong Xiang & Shiming Wang, 2019. "A New 4D Chaotic System with Two-Wing, Four-Wing, and Coexisting Attractors and Its Circuit Simulation," Complexity, Hindawi, vol. 2019, pages 1-13, October.
    9. Cheng, Wei & Wang, Yan & Peng, Zheng & Ren, Xiaodong & Shuai, Yubei & Zang, Shengyin & Liu, Hao & Cheng, Hao & Wu, Jiagui, 2021. "High-efficiency chaotic time series prediction based on time convolution neural network," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
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