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Adaptive Broad Echo State Network for Nonstationary Time Series Forecasting

Author

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  • Wen-Jie Liu

    (School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China
    Beijing Laboratory for Intelligent Environmental Protection, Beijing Technology and Business University, Beijing 100048, China
    State Environmental Protection Key Laboratory of Food Chain Pollution Control, Beijing Technology and Business University, Beijing 100048, China)

  • Yu-Ting Bai

    (School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China
    Beijing Laboratory for Intelligent Environmental Protection, Beijing Technology and Business University, Beijing 100048, China
    State Environmental Protection Key Laboratory of Food Chain Pollution Control, Beijing Technology and Business University, Beijing 100048, China)

  • Xue-Bo Jin

    (School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China
    Beijing Laboratory for Intelligent Environmental Protection, Beijing Technology and Business University, Beijing 100048, China
    State Environmental Protection Key Laboratory of Food Chain Pollution Control, Beijing Technology and Business University, Beijing 100048, China)

  • Ting-Li Su

    (School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China
    Beijing Laboratory for Intelligent Environmental Protection, Beijing Technology and Business University, Beijing 100048, China
    State Environmental Protection Key Laboratory of Food Chain Pollution Control, Beijing Technology and Business University, Beijing 100048, China)

  • Jian-Lei Kong

    (School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China
    Beijing Laboratory for Intelligent Environmental Protection, Beijing Technology and Business University, Beijing 100048, China
    State Environmental Protection Key Laboratory of Food Chain Pollution Control, Beijing Technology and Business University, Beijing 100048, China)

Abstract

Time series forecasting provides a vital basis for the control and management of various systems. The time series data in the real world are usually strongly nonstationary and nonlinear, which increases the difficulty of reliable forecasting. To fully utilize the learning capability of machine learning in time series forecasting, an adaptive broad echo state network (ABESN) is proposed in this paper. Firstly, the broad learning system (BLS) is used as a framework, and the reservoir pools in the echo state network (ESN) are introduced to form the broad echo state network (BESN). Secondly, for the problem of information redundancy in the reservoir structure in BESN, an adaptive optimization algorithm for the BESN structure based on the pruning algorithm is proposed. Thirdly, an adaptive optimization algorithm of hyperparameters based on the nonstationary test index is proposed. In brief, the structure and hyperparameter optimization algorithms are studied to form the ABESN based on the proposed BESN model in this paper. The ABESN is applied to the data forecasting of air humidity and electric load. The experiments show that the proposed ABESN has a better learning ability for nonstationary time series data and can achieve higher forecasting accuracy.

Suggested Citation

  • Wen-Jie Liu & Yu-Ting Bai & Xue-Bo Jin & Ting-Li Su & Jian-Lei Kong, 2022. "Adaptive Broad Echo State Network for Nonstationary Time Series Forecasting," Mathematics, MDPI, vol. 10(17), pages 1-21, September.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:17:p:3188-:d:906203
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    References listed on IDEAS

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