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Volume 10 Issue 8
Aug.  2023

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2023  >  10(8): 1743-1753
X. F. Chen, M. Liu, and  S. Li,  “Echo state network with probabilistic regularization for time series prediction,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 8, pp. 1743–1753, Aug. 2023. doi: 10.1109/JAS.2023.123489
Citation: X. F. Chen, M. Liu, and  S. Li,  “Echo state network with probabilistic regularization for time series prediction,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 8, pp. 1743–1753, Aug. 2023. doi: 10.1109/JAS.2023.123489
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Echo State Network With Probabilistic Regularization for Time Series Prediction

doi: 10.1109/JAS.2023.123489
Funds:  This work was supported in part by the National Natural Science Foundation of China (62176109), the CAAI-Huawei MindSpore Open Fund (CAAIXSJLJJ-2022-020A), the Natural Science Foundation of Gansu Province (21JR7RA531, 22JR5RA427, 22JR5RA487), the Fundamental Research Funds for the Central Universities (lzujbky-2022-kb12, lzujbky-2022-23), the Science and Technology Project of Chengguan Discrict of Lanzhou (2021-1-2), and the Supercomputing Center of Lanzhou University
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    Abstract

  • Recent decades have witnessed a trend that the echo state network (ESN) is widely utilized in field of time series prediction due to its powerful computational abilities. However, most of the existing research on ESN is conducted under the assumption that data is free of noise or polluted by the Gaussian noise, which lacks robustness or even fails to solve real-world tasks. This work handles this issue by proposing a probabilistic regularized ESN (PRESN) with robustness guaranteed. Specifically, we design a novel objective function for minimizing both the mean and variance of modeling error, and then a scheme is derived for getting output weights of the PRESN. Furthermore, generalization performance, robustness, and unbiased estimation abilities of the PRESN are revealed by theoretical analyses. Finally, experiments on a benchmark dataset and two real-world datasets are conducted to verify the performance of the proposed PRESN. The source code is publicly available at https://github.com/LongJin-lab/probabilistic-regularized-echo-state-network.

     

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    Highlights

    • It focuses on putting forward an improved echo state network for predicting time series in the presence of various kinds of noises
    • The mean and variance of the modeling error are minimized by optimizing the constructed objective function in the proposed model
    • It conducts experiments on a benchmark dataset as well as two real-world ones and comparisons based on different prediction models to verify the effectiveness and superiority of the proposed model

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