IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i22p4264-d972851.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/22/4264/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/10/22/4264/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jinrui Zang & Pengpeng Jiao & Guohua Song & Zhihong Li & Tingyi Peng, 2022. "A Novel Environment Estimation Method of Whole Sample Traffic Flows and Emissions Based on Multifactor MFD," IJERPH, MDPI, vol. 19(24), pages 1-26, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Qin Lu & Jingwen Liao & Kechi Chen & Yanhui Liang & Yu Lin, 2024. "Predicting Natural Gas Prices Based on a Novel Hybrid Model with Variational Mode Decomposition," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 639-678, February.
    2. Hasnain Iftikhar & Josue E. Turpo-Chaparro & Paulo Canas Rodrigues & Javier Linkolk López-Gonzales, 2023. "Forecasting Day-Ahead Electricity Prices for the Italian Electricity Market Using a New Decomposition—Combination Technique," Energies, MDPI, vol. 16(18), pages 1-23, September.
    3. Jun Dong & Xihao Dou & Aruhan Bao & Yaoyu Zhang & Dongran Liu, 2022. "Day-Ahead Spot Market Price Forecast Based on a Hybrid Extreme Learning Machine Technique: A Case Study in China," Sustainability, MDPI, vol. 14(13), pages 1-24, June.
    4. Yang, Haolin & Schell, Kristen R., 2022. "GHTnet: Tri-Branch deep learning network for real-time electricity price forecasting," Energy, Elsevier, vol. 238(PC).
    5. Wang, Bin & Wang, Jun, 2021. "Energy futures price prediction and evaluation model with deep bidirectional gated recurrent unit neural network and RIF-based algorithm," Energy, Elsevier, vol. 216(C).
    6. Gabrielli, Paolo & Wüthrich, Moritz & Blume, Steffen & Sansavini, Giovanni, 2022. "Data-driven modeling for long-term electricity price forecasting," Energy, Elsevier, vol. 244(PB).
    7. Nie, Ying & Li, Ping & Wang, Jianzhou & Zhang, Lifang, 2024. "A novel multivariate electrical price bi-forecasting system based on deep learning, a multi-input multi-output structure and an operator combination mechanism," Applied Energy, Elsevier, vol. 366(C).
    8. Ghimire, Sujan & Deo, Ravinesh C. & Casillas-Pérez, David & Salcedo-Sanz, Sancho, 2024. "Two-step deep learning framework with error compensation technique for short-term, half-hourly electricity price forecasting," Applied Energy, Elsevier, vol. 353(PA).
    9. Zeyu Wang & Yue Deng, 2022. "Optimizing Financial Engineering Time Indicator Using Bionics Computation Algorithm and Neural Network Deep Learning," Computational Economics, Springer;Society for Computational Economics, vol. 59(4), pages 1755-1772, April.
    10. Li, Jinlong & Shi, Xilin & Zhang, Shuai, 2020. "Construction modeling and parameter optimization of multi-step horizontal energy storage salt caverns," Energy, Elsevier, vol. 203(C).
    11. AL-Alimi, Dalal & AlRassas, Ayman Mutahar & Al-qaness, Mohammed A.A. & Cai, Zhihua & Aseeri, Ahmad O. & Abd Elaziz, Mohamed & Ewees, Ahmed A., 2023. "TLIA: Time-series forecasting model using long short-term memory integrated with artificial neural networks for volatile energy markets," Applied Energy, Elsevier, vol. 343(C).
    12. Xiong, Xiaoping & Qing, Guohua, 2023. "A hybrid day-ahead electricity price forecasting framework based on time series," Energy, Elsevier, vol. 264(C).
    13. Liu, Kailei & Cheng, Jinhua & Yi, Jiahui, 2022. "Copper price forecasted by hybrid neural network with Bayesian Optimization and wavelet transform," Resources Policy, Elsevier, vol. 75(C).
    14. Heidarpanah, Mohammadreza & Hooshyaripor, Farhad & Fazeli, Meysam, 2023. "Daily electricity price forecasting using artificial intelligence models in the Iranian electricity market," Energy, Elsevier, vol. 263(PE).
    15. Saâdaoui, Foued & Ben Jabeur, Sami, 2023. "Analyzing the influence of geopolitical risks on European power prices using a multiresolution causal neural network," Energy Economics, Elsevier, vol. 124(C).
    16. Huei-Wen Teng & Yu-Hsien Li, 2023. "Can deep neural networks outperform Fama-MacBeth regression and other supervised learning approaches in stock returns prediction with asset-pricing factors?," Digital Finance, Springer, vol. 5(1), pages 149-182, March.
    17. Sajjad Khan & Shahzad Aslam & Iqra Mustafa & Sheraz Aslam, 2021. "Short-Term Electricity Price Forecasting by Employing Ensemble Empirical Mode Decomposition and Extreme Learning Machine," Forecasting, MDPI, vol. 3(3), pages 1-18, June.
    18. Lan, Hai & Zheng, Puyang & Li, Zheng, 2021. "Constructing urban sprawl measurement system of the Yangtze River economic belt zone for healthier lives and social changes in sustainable cities," Technological Forecasting and Social Change, Elsevier, vol. 165(C).
    19. Huaiyu Liu & Zhijun Meng & Anqi Zhang & Yue Cong & Xiaofei An & Weiqiang Fu & Guangwei Wu & Yanxin Yin & Chengqian Jin, 2022. "On-Line Detection Method and Device for Moisture Content Measurement of Bales in a Square Baler," Agriculture, MDPI, vol. 12(8), pages 1-16, August.
    20. Dushmanta Kumar Padhi & Neelamadhab Padhy & Akash Kumar Bhoi & Jana Shafi & Muhammad Fazal Ijaz, 2021. "A Fusion Framework for Forecasting Financial Market Direction Using Enhanced Ensemble Models and Technical Indicators," Mathematics, MDPI, vol. 9(21), pages 1-31, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:22:p:4264-:d:972851. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.