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Prediction of Soybean Price Trend via a Synthesis Method With Multistage Model

Author

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  • Zhiling Xu

    (Northeast Agricultural University, Harbin, China)

  • Hualing Deng

    (Northeast Agricultural University, Harbin, China)

  • Qiufeng Wu

    (Northeast Agricultural University, Harbin, China)

Abstract

Soybean is an important crop, so it is very important to forecast soybean price trend, which can stabilize the market. This paper presents a Synthesis Method with Multistage Model (SMwMM) in order to identify and forecast soybean price trend in China. In the previous work,Toeplitz Inverse Covariance-based Clustering(TICC) has been applied to cluster the prices of four variables. The research have found that there are four patterns in soybean market price, which could be explained by economic theory. This paper consider four patterns as market risk levels. Based on the clustering results, we used Long short-term memory(LSTM) to forecast the prices of these four variables. Multivariate long short-term memory(MLSTM) is then used to classify soybean price to determine level of risk . Experimental results show that :(1)The LSTM model has achieved great fitting effect and high prediction accuracy;(2) The performance of MLSTM-FCN and MALSTM-FCN is better than that of LSTM-FCN and ALSTM-FCN. Furthermore,MALSTM-FCN had the higher accuracy than MLSTM-FCN, which reached 76.39%.

Suggested Citation

  • Zhiling Xu & Hualing Deng & Qiufeng Wu, 2021. "Prediction of Soybean Price Trend via a Synthesis Method With Multistage Model," International Journal of Agricultural and Environmental Information Systems (IJAEIS), IGI Global, vol. 12(4), pages 1-13, October.
    • Handle: RePEc:igg:jaeis0:v:12:y:2021:i:4:p:1-13
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    References listed on IDEAS

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    1. Hua Ling Deng & Yǔ Qiàn Sūn, 2019. "Soybean Price Pattern Discovery Via Toeplitz Inverse Covariance-Based Clustering," International Journal of Agricultural and Environmental Information Systems (IJAEIS), IGI Global, vol. 10(4), pages 1-17, October.
    2. Krzysztof Drachal, 2019. "Analysis of Agricultural Commodities Prices with New Bayesian Model Combination Schemes," Sustainability, MDPI, vol. 11(19), pages 1-23, September.
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