IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v13y2023i9p1671-d1224070.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/13/9/1671/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/13/9/1671/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Helin Yin & Dong Jin & Yeong Hyeon Gu & Chang Jin Park & Sang Keun Han & Seong Joon Yoo, 2020. "STL-ATTLSTM: Vegetable Price Forecasting Using STL and Attention Mechanism-Based LSTM," Agriculture, MDPI, vol. 10(12), pages 1-17, December.
    2. Yan Guo & Dezhao Tang & Wei Tang & Senqi Yang & Qichao Tang & Yang Feng & Fang Zhang, 2022. "Agricultural Price Prediction Based on Combined Forecasting Model under Spatial-Temporal Influencing Factors," Sustainability, MDPI, vol. 14(17), pages 1-18, August.
    3. Crespo Cuaresma, Jesús & Hlouskova, Jaroslava & Kossmeier, Stephan & Obersteiner, Michael, 2004. "Forecasting electricity spot-prices using linear univariate time-series models," Applied Energy, Elsevier, vol. 77(1), pages 87-106, January.
    4. Ronald W. Ward, 1982. "Asymmetry in Retail, Wholesale, and Shipping Point Pricing for Fresh Vegetables," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 64(2), pages 205-212.
    5. Puchalsky, Weslly & Ribeiro, Gabriel Trierweiler & da Veiga, Claudimar Pereira & Freire, Roberto Zanetti & Santos Coelho, Leandro dos, 2018. "Agribusiness time series forecasting using Wavelet neural networks and metaheuristic optimization: An analysis of the soybean sack price and perishable products demand," International Journal of Production Economics, Elsevier, vol. 203(C), pages 174-189.
    6. Sun, Wei & Huang, Chenchen, 2020. "A novel carbon price prediction model combines the secondary decomposition algorithm and the long short-term memory network," Energy, Elsevier, vol. 207(C).
    7. Liwen Ling & Dabin Zhang & Amin W. Mugera & Shanying Chen & Qiang Xia, 2019. "A Forecast Combination Framework with Multi-Time Scale for Livestock Products’ Price Forecasting," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-11, October.
    8. Kai Ye & Yangheran Piao & Kun Zhao & Xiaohui Cui, 2021. "A Heterogeneous Graph Enhanced LSTM Network for Hog Price Prediction Using Online Discussion," Agriculture, MDPI, vol. 11(4), pages 1-14, April.
    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. Arthanus Mutuku & Peter Murage & Stanley Sewe, 2024. "Application of SARIMAX model to forecast weekly Irish potato retail prices: a case study of Kitui County, Kenya," SN Business & Economics, Springer, vol. 4(11), pages 1-28, November.
    2. Rui-Feng Wang & Wen-Hao Su, 2024. "The Application of Deep Learning in the Whole Potato Production Chain: A Comprehensive Review," Agriculture, MDPI, vol. 14(8), pages 1-30, July.
    3. Xiaohong Yu & Bin Liu & Yongzeng Lai, 2024. "Monthly Pork Price Prediction Applying Projection Pursuit Regression: Modeling, Empirical Research, Comparison, and Sustainability Implications," Sustainability, MDPI, vol. 16(4), pages 1-26, February.
    4. 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.

    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. Youzhu Li & Jinsi Liu & Hongyu Yang & Jianxin Chen & Jason Xiong, 2021. "A Bibliometric Analysis of Literature on Vegetable Prices at Domestic and International Markets—A Knowledge Graph Approach," Agriculture, MDPI, vol. 11(10), pages 1-17, September.
    2. Marchetti, Isabella & Rego, Erik Eduardo, 2022. "The impact of hourly pricing for renewable generation projects in Brazil," Renewable Energy, Elsevier, vol. 189(C), pages 601-617.
    3. Islam Hassouneh & Teresa Serra & José M. Gil, 2010. "Price transmission in the Spanish bovine sector: the BSE effect," Agricultural Economics, International Association of Agricultural Economists, vol. 41(1), pages 33-42, January.
    4. Sinha, Pankaj & Mathur, Kritika, 2016. "Empirical Analysis of Developments in the Day Ahead Electricity Markets in India," MPRA Paper 72969, University Library of Munich, Germany.
    5. Billé, Anna Gloria & Gianfreda, Angelica & Del Grosso, Filippo & Ravazzolo, Francesco, 2023. "Forecasting electricity prices with expert, linear, and nonlinear models," International Journal of Forecasting, Elsevier, vol. 39(2), pages 570-586.
    6. Gao, Feng & Shao, Xueyan, 2022. "A novel interval decomposition ensemble model for interval carbon price forecasting," Energy, Elsevier, vol. 243(C).
    7. Michel Simioni & Frédéric Gonzales & Patrice Guillotreau & Laurent Le Grel, 2013. "Detecting Asymmetric Price Transmission with Consistent Threshold along the Fish Supply Chain," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 61(1), pages 37-60, March.
    8. Thao Pham & Killian Lemoine, 2020. "Impacts of subsidized renewable electricity generation on spot market prices in Germany : Evidence from a GARCH model with panel data," Working Papers hal-02568268, HAL.
    9. Piotr Bórawski & Marta Guth & Andrzej Parzonko & Tomasz Rokicki & Aleksandra Perkowska & James William Dunn, 2021. "Price volatility of milk and dairy products in Poland after accession to the EU," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 67(3), pages 111-119.
    10. Rafal Weron & Adam Misiorek, 2006. "Short-term electricity price forecasting with time series models: A review and evaluation," HSC Research Reports HSC/06/01, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    11. Streckiene, Giedre & Martinaitis, Vytautas & Andersen, Anders N. & Katz, Jonas, 2009. "Feasibility of CHP-plants with thermal stores in the German spot market," Applied Energy, Elsevier, vol. 86(11), pages 2308-2316, November.
    12. Richards, Timothy J. & Patterson, Paul M., 2000. "New Varieties And The Returns To Commodity Promotion: The Case Of Fuji Apples," Agricultural and Resource Economics Review, Northeastern Agricultural and Resource Economics Association, vol. 29(1), pages 1-14, April.
    13. Fong Chan, Kam & Gray, Philip, 2006. "Using extreme value theory to measure value-at-risk for daily electricity spot prices," International Journal of Forecasting, Elsevier, vol. 22(2), pages 283-300.
    14. Marie Bessec & Julien Fouquau & Sophie Meritet, 2016. "Forecasting electricity spot prices using time-series models with a double temporal segmentation," Applied Economics, Taylor & Francis Journals, vol. 48(5), pages 361-378, January.
    15. Xavier Serrano-Guerrero & Guillermo Escrivá-Escrivá & Santiago Luna-Romero & Jean-Michel Clairand, 2020. "A Time-Series Treatment Method to Obtain Electrical Consumption Patterns for Anomalies Detection Improvement in Electrical Consumption Profiles," Energies, MDPI, vol. 13(5), pages 1-23, February.
    16. Afanasyev, Dmitriy O. & Fedorova, Elena A., 2019. "On the impact of outlier filtering on the electricity price forecasting accuracy," Applied Energy, Elsevier, vol. 236(C), pages 196-210.
    17. Simon Pezzutto & Gianluca Grilli & Stefano Zambotti & Stefan Dunjic, 2018. "Forecasting Electricity Market Price for End Users in EU28 until 2020—Main Factors of Influence," Energies, MDPI, vol. 11(6), pages 1-18, June.
    18. Irz, Xavier & Mazzocchi, Mario & Réquillart, Vincent & Soler, Louis-Georges, 2015. "Research in Food Economics: past trends and new challenges," Revue d'Etudes en Agriculture et Environnement, Editions NecPlus, vol. 96(01), pages 187-237, March.
    19. Junhao Wu & Yuan Hu & Daqing Wu & Zhengyong Yang, 2022. "An Aquatic Product Price Forecast Model Using VMD-IBES-LSTM Hybrid Approach," Agriculture, MDPI, vol. 12(8), pages 1-26, August.
    20. Aysoy, Cevriye & Kirli, Duygu Halim & Tumen, Semih, 2015. "How does a shorter supply chain affect pricing of fresh food? Evidence from a natural experiment," Food Policy, Elsevier, vol. 57(C), pages 104-113.

    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:jagris:v:13:y:2023:i:9:p:1671-:d:1224070. 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.