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Agricultural Product Price Forecasting Methods: A Review

Author

Listed:
  • Feihu Sun

    (School of Information Science and Engineering, Shandong Agricultural University, Taian 271018, China
    Key Laboratory of Huang-Huai-Hai Smart Agricultural Technology, Ministry of Agriculture and Rural Affairs, Taian 271018, China
    Agricultural Big-Data Research Center, Shandong Agricultural University, Taian 271018, China)

  • Xianyong Meng

    (School of Information Science and Engineering, Shandong Agricultural University, Taian 271018, China
    Key Laboratory of Huang-Huai-Hai Smart Agricultural Technology, Ministry of Agriculture and Rural Affairs, Taian 271018, China
    Agricultural Big-Data Research Center, Shandong Agricultural University, Taian 271018, China)

  • Yan Zhang

    (School of Information Science and Engineering, Shandong Agricultural University, Taian 271018, China
    Key Laboratory of Huang-Huai-Hai Smart Agricultural Technology, Ministry of Agriculture and Rural Affairs, Taian 271018, China
    Agricultural Big-Data Research Center, Shandong Agricultural University, Taian 271018, China)

  • Yan Wang

    (School of Information Science and Engineering, Shandong Agricultural University, Taian 271018, China
    Key Laboratory of Huang-Huai-Hai Smart Agricultural Technology, Ministry of Agriculture and Rural Affairs, Taian 271018, China
    Agricultural Big-Data Research Center, Shandong Agricultural University, Taian 271018, China)

  • Hongtao Jiang

    (School of Information Science and Engineering, Shandong Agricultural University, Taian 271018, China
    Key Laboratory of Huang-Huai-Hai Smart Agricultural Technology, Ministry of Agriculture and Rural Affairs, Taian 271018, China
    Agricultural Big-Data Research Center, Shandong Agricultural University, Taian 271018, China)

  • Pingzeng Liu

    (School of Information Science and Engineering, Shandong Agricultural University, Taian 271018, China
    Key Laboratory of Huang-Huai-Hai Smart Agricultural Technology, Ministry of Agriculture and Rural Affairs, Taian 271018, China
    Agricultural Big-Data Research Center, Shandong Agricultural University, Taian 271018, China)

Abstract
Agricultural price prediction is a hot research topic in the field of agriculture, and accurate prediction of agricultural prices is crucial to realize the sustainable and healthy development of agriculture. It explores traditional forecasting methods, intelligent forecasting methods, and combination model forecasting methods, and discusses the challenges faced in the current research landscape of agricultural commodity price prediction. The results of the study show that: (1) The use of combined models for agricultural product price forecasting is a future development trend, and exploring the combination principle of the models is a key to realize accurate forecasting; (2) the integration of the combination of structured data and unstructured variable data into the models for price forecasting is a future development trend; and (3) in the prediction of agricultural product prices, both the accuracy of the values and the precision of the trends should be ensured. This paper reviews and analyzes the methods of agricultural product price prediction and expects to provide some help for the development of research in this field.

Suggested Citation

  • Feihu Sun & Xianyong Meng & Yan Zhang & Yan Wang & Hongtao Jiang & Pingzeng Liu, 2023. "Agricultural Product Price Forecasting Methods: A Review," Agriculture, MDPI, vol. 13(9), pages 1-20, August.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:9:p:1671-:d:1224070
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    References listed on IDEAS

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    Cited by:

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    6. Hamid Ahaggach & Lylia Abrouk & Eric Lebon, 2024. "Systematic Mapping Study of Sales Forecasting: Methods, Trends, and Future Directions," Forecasting, MDPI, vol. 6(3), pages 1-31, July.

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