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A Novel Agricultural Commodity Price Forecasting Model Based on Fuzzy Information Granulation and MEA-SVM Model

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  • Yongli Zhang
  • Sanggyun Na

Abstract

Accurately predicting the price of agricultural commodity is very important for evading market risk, increasing agricultural income, and accomplishing government macroeconomic regulation. With the price index predictions of 6 commodities of Food and Agriculture Organization of the United Nations (FAO) as examples, this paper proposed a novel agricultural commodity price forecasting model which combined the fuzzy information granulation, mind evolutionary algorithm (MEA), and support vector machine (SVM). Firstly, the time series data of agricultural commodity price index was transformed into fuzzy information granulation particles made up of Low , , and Up , which represented the trend and magnitude of price movement. Secondly, MEA algorithm was employed to seek the optimal parameters and for SVM to establish the MEA-SVM model. Finally, FOA price index fluctuation range and change trend in the future were predicted by the MEA-SVM model. The empirical analysis showed that the MEA-SVM model was effective and had higher prediction accuracy and faster calculation speed in the forecasting of agricultural commodity price.

Suggested Citation

  • Yongli Zhang & Sanggyun Na, 2018. "A Novel Agricultural Commodity Price Forecasting Model Based on Fuzzy Information Granulation and MEA-SVM Model," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-10, November.
    • Handle: RePEc:hin:jnlmpe:2540681
    DOI: 10.1155/2018/2540681
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    Cited by:

    1. Qingjie Zhou & Panpan Zhu & You Wu & Yinpeng Zhang, 2022. "Research on the Volatility of the Cotton Market under Different Term Structures: Perspective from Investor Attention," Sustainability, MDPI, vol. 14(21), pages 1-20, November.
    2. Tserenpurev Chuluunsaikhan & Ga-Ae Ryu & Kwan-Hee Yoo & HyungChul Rah & Aziz Nasridinov, 2020. "Incorporating Deep Learning and News Topic Modeling for Forecasting Pork Prices: The Case of South Korea," Agriculture, MDPI, vol. 10(11), pages 1-22, October.
    3. Qi Zhang & Yi Hu & Jianbin Jiao & Shouyang Wang, 2022. "Exploring the Trend of Commodity Prices: A Review and Bibliometric Analysis," Sustainability, MDPI, vol. 14(15), pages 1-22, August.
    4. Prabakaran, G. & Vaithiyanathan, D. & Ganesan, Madhavi, 2021. "FPGA based effective agriculture productivity prediction system using fuzzy support vector machine," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 185(C), pages 1-16.
    5. Roberto Louis Forestal & Shih-Ming Pi, 2021. "Using Artificial Neural networks and Optimal Scaling Model to Forecast Agriculture Commodity Price: An Ecological-economic Approach," Advances in Management and Applied Economics, SCIENPRESS Ltd, vol. 11(3), pages 1-3.

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