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A Novel Bézier LSTM Model: A Case Study in Corn Analysis

Author

Listed:
  • Qingliang Zhao

    (College of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China)

  • Junji Chen

    (College of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China)

  • Xiaobin Feng

    (College of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China)

  • Yiduo Wang

    (School of Mathematics and Physics, Beijing University of Chemical Technology, Beijing 100029, China)

Abstract

Accurate prediction of agricultural product prices is instrumental in providing rational guidance for agricultural production planning and the development of the agricultural industry. By constructing an end-to-end agricultural product price prediction model, incorporating a segmented Bézier curve fitting algorithm and Long Short-Term Memory (LSTM) network, this study selects corn futures prices listed on the Dalian Commodity Exchange as the research subject to predict and validate their price trends. Firstly, corn futures prices are fitted using segmented Bézier curves. Subsequently, the fitted price sequence is employed as a feature and input into an LSTM network for training to obtain a price prediction model. Finally, the prediction results of the Bézier curve-based LSTM model are compared and analyzed with traditional LSTM, ARIMA (Autoregressive Integrated Moving Average Model), VMD-LSTM, and SVR (Support Vector Regression) models. The research findings indicate that the proposed Bézier curve-based LSTM model demonstrates significant predictive advantages in corn futures price prediction. Through comparison with traditional models, the effectiveness of this model is affirmed. Consequently, the Bézier curve-based LSTM model proposed in this paper can serve as a crucial reference for agricultural product price prediction, providing effective guidance for agricultural production planning and industry development.

Suggested Citation

  • Qingliang Zhao & Junji Chen & Xiaobin Feng & Yiduo Wang, 2024. "A Novel Bézier LSTM Model: A Case Study in Corn Analysis," Mathematics, MDPI, vol. 12(15), pages 1-21, July.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:15:p:2308-:d:1441199
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    References listed on IDEAS

    as
    1. Kenneth Gilbert, 2005. "An ARIMA Supply Chain Model," Management Science, INFORMS, vol. 51(2), pages 305-310, February.
    2. Tao Xiong & Miao Li & Jia Cao, 2023. "Do Futures Prices Help Forecast Spot Prices? Evidence from China’s New Live Hog Futures," Agriculture, MDPI, vol. 13(9), pages 1-16, August.
    3. Domenico Piccolo, 1990. "A Distance Measure For Classifying Arima Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 11(2), pages 153-164, March.
    4. Qingliang Zhao & Xiaobin Feng & Liwen Zhang & Yiduo Wang, 2023. "Research on Short-Term Passenger Flow Prediction of LSTM Rail Transit Based on Wavelet Denoising," Mathematics, MDPI, vol. 11(19), pages 1-16, October.
    5. Abdulkadir Atalan, 2023. "Forecasting drinking milk price based on economic, social, and environmental factors using machine learning algorithms," Agribusiness, John Wiley & Sons, Ltd., vol. 39(1), pages 214-241, January.
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