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Prediction of China’s Polysilicon Prices: A Combination Model Based on Variational Mode Decomposition, Sparrow Search Algorithm and Long Short-Term Memory

Author

Listed:
  • Jining Wang

    (School of Economics and Management, Nanjing Tech University, Nanjing 211816, China
    These authors contributed equally to this work.)

  • Lin Jiang

    (School of Economics and Management, Nanjing Tech University, Nanjing 211816, China
    These authors contributed equally to this work.)

  • Lei Wang

    (School of Economics and Management, Nanjing Tech University, Nanjing 211816, China
    These authors contributed equally to this work.)

Abstract

Given the non-stationarity, nonlinearity, and high complexity of polysilicon prices in the photovoltaic (PV) industry chain, this paper introduces upstream and downstream material prices of the PV industry chain and macroeconomic indicators as influencing factors. The VMD–SSA–LSTM combination model is constructed to predict polysilicon prices, which is based on Variational Mode Decomposition (VMD) and utilizes the Sparrow Search Algorithm (SSA) to optimize the Long Short-Term Memory (LSTM) network. The research shows that decomposing the original polysilicon time series using the VMD algorithm effectively extracts the main features of polysilicon price data, reducing data instability. By optimizing the learning rate, hidden layer nodes, and regularization coefficients of the LSTM model using the Sparrow Search Algorithm, the model achieves higher convergence accuracy. Compared to the traditional LSTM model and VMD–LSTM model, the VMD–SSA–LSTM model exhibits the smallest error and the highest goodness-of-fit on the polysilicon dataset, demonstrating higher predictive accuracy for polysilicon prices, which provides more accurate reference data for market analysis and pricing decisions of the polysilicon industry.

Suggested Citation

  • Jining Wang & Lin Jiang & Lei Wang, 2024. "Prediction of China’s Polysilicon Prices: A Combination Model Based on Variational Mode Decomposition, Sparrow Search Algorithm and Long Short-Term Memory," Mathematics, MDPI, vol. 12(23), pages 1-14, November.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:23:p:3690-:d:1529050
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    References listed on IDEAS

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    1. Liu, Wenhui & Bai, Yulong & Yue, Xiaoxin & Wang, Rui & Song, Qi, 2024. "A wind speed forcasting model based on rime optimization based VMD and multi-headed self-attention-LSTM," Energy, Elsevier, vol. 294(C).
    2. Lei, Lei & Shao, Suola & Liang, Lixia, 2024. "An evolutionary deep learning model based on EWKM, random forest algorithm, SSA and BiLSTM for building energy consumption prediction," Energy, Elsevier, vol. 288(C).
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    Cited by:

    1. Wenyi Wang & Haifei Liu & Lin Jiang & Lei Wang, 2025. "Prediction of Lithium Carbonate Prices in China Applying a VMD–SSA–LSTM Combined Model," Mathematics, MDPI, vol. 13(4), pages 1-17, February.

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