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Design of a bi-level PSO based modular neural network for multi-step time series prediction

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Abstract

Derived from an effective strategy - direct and multiple-input multiple-output strategy, a modular neural network based on a bi-level particle swarm optimization algorithm (BLPSO-MNN) is proposed in the present study to improve the accuracy for multi-step time series prediction. While a binary particle swarm optimization algorithm is designed for the external layer to optimize the task division of prediction horizons, a multi-objective particle swarm optimization algorithm is designed for the internal layer to trade off between the prediction accuracy and structural complexity for each subnetwork in modular neural network. Besides, a set of fuzzy If-Then rules is proposed to determine the historical information to be input to subnetworks. Thus, the structure of BLPSO-MNN, including the number of modules as well as the subnetwork structure, is self-determined accordingly. Numerous experiments are conducted for 18-step-ahead time series prediction to evaluate the performance of BLPSO-MNN. Experimental results show that, although the prediction accuracy decreases when the prediction horizon is large, the overall performance of BLPSO-MNN is superior over all comparative models with greater improvement for larger horizons, indicating it is suitable for a long-term prediction. Besides, the set of fuzzy rules balances the prediction accuracy against the structural complexity caused by the subnetwork inputs.

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Data Availibility Statement

1. The dataset 3 analyzed during the current study is from the World Data Centre. [http://sidc.oma.be/silso/] 2. The datasets generated during the current study are available from the corresponding author on reasonable request.

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Funding

This work was supported by the National Natural Science Foundation of China (Grant Nos. 62173008 and 62021003).

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Authors and Affiliations

  1. Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China

    Wenjing Li, Yonglei Liu & Zhiqian Chen

  2. Beijing Institute of Artificial Intelligence, Beijing, 100124, China

    Wenjing Li, Yonglei Liu & Zhiqian Chen

  3. Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, 100124, China

    Wenjing Li, Yonglei Liu & Zhiqian Chen

  4. Beijing Laboratory for Intelligent Environmental Protection, Beijing, 100124, China

    Wenjing Li, Yonglei Liu & Zhiqian Chen

Authors
  1. Wenjing Li
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  2. Yonglei Liu
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Contributions

Wenjing LI: Funding acquisition, Methodology, Writing-original draft, Writing - review & editing, Software. Yonglei LIU: Conceptualization, Software, Investigation. Zhiqian CHEN: Data curation, Software.

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Correspondence to Wenjing Li.

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Li, W., Liu, Y. & Chen, Z. Design of a bi-level PSO based modular neural network for multi-step time series prediction. Appl Intell 54, 8612–8633 (2024). https://doi.org/10.1007/s10489-024-05638-0

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