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Prediction of Natural Rubber Customs Declaration Price Based on Wavelet Decomposition and GA-BP Neural Network Group

Author

Listed:
  • Hongjie Yi

    (School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266033, China)

  • Ke Zhang

    (School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266033, China)

  • Kun Ma

    (Qingdao Customs, Qingdao 266071, China)

  • Lijian Zhou

    (School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266033, China)

  • Futong Tang

    (School of Information and Control Engineering, Qingdao University of Technology, Qingdao 266033, China)

Abstract

Natural rubber is mainly dependent on import in China, its domestic market price is influenced by the Natural Rubber Customs Declaration Price (NRCDP). Considering the fluctuating properties of the NRCDP, a method of the NRCDP based on Wavelet and the optimized Back Propagation (BP) neural network Group using a Genetic Algorithm (W-GA-BPG) is proposed. First, an NRCDP dataset is established based on the original Customs Declaration Price (CDP) dataset collected by Qingdao Customs, in which the commodity types are selected consistently according to the sampling intervals, and the features are deleted if they are less affected by the fluctuation of NRCDP. Secondly, the selected features in NRCDP are decomposed using wavelet transform to obtain a group of feature sequences with different scales. Then, a Group of BP neural networks (BPG) optimized by Genetic Algorithm (GA) is used to predict multiple decomposition sub-sequences, respectively. Finally, the predicted values are obtained through wavelet reconstruction. Combined with the NRCDP dataset, the W-GA-BPG model is established by comparing and analyzing experiments by evaluating the Mean Square Error (MSE) and determination coefficient of the prediction results. The MSE and determination coefficient predicted using the proposed model are 0.0043 and 0.9302, respectively, which is the best prediction effect.

Suggested Citation

  • Hongjie Yi & Ke Zhang & Kun Ma & Lijian Zhou & Futong Tang, 2022. "Prediction of Natural Rubber Customs Declaration Price Based on Wavelet Decomposition and GA-BP Neural Network Group," Mathematics, MDPI, vol. 10(22), pages 1-15, November.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:22:p:4264-:d:972851
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    References listed on IDEAS

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    1. Pengyue Wang & Xuesheng Li & Zhiliang Qin & Yuanyuan Qu & Zhongkai Zhang & Muazzam Maqsood, 2022. "Stock Price Forecasting Based on Wavelet Filtering and Ensembled Machine Learning Model," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-12, June.
    2. Qiao, Weibiao & Yang, Zhe, 2020. "Forecast the electricity price of U.S. using a wavelet transform-based hybrid model," Energy, Elsevier, vol. 193(C).
    3. Roman Tkachenko & Ivan Izonin & Pavlo Vitynskyi & Nataliia Lotoshynska & Olena Pavlyuk, 2018. "Development of the Non-Iterative Supervised Learning Predictor Based on the Ito Decomposition and SGTM Neural-Like Structure for Managing Medical Insurance Costs," Data, MDPI, vol. 3(4), pages 1-14, October.
    4. Wenguang Yu & Guofeng Guan & Jingchao Li & Qi Wang & Xiaohan Xie & Yu Zhang & Yujuan Huang & Xinliang Yu & Chaoran Cui & Benjamin Miranda Tabak, 2021. "Claim Amount Forecasting and Pricing of Automobile Insurance Based on the BP Neural Network," Complexity, Hindawi, vol. 2021, pages 1-17, January.
    5. Yu, Zhuoxi & Qin, Lu & Chen, Yunjing & Parmar, Milan Deepak, 2020. "Stock price forecasting based on LLE-BP neural network model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 553(C).
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