IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v11y2021i4p359-d537200.html
   My bibliography  Save this article

A Heterogeneous Graph Enhanced LSTM Network for Hog Price Prediction Using Online Discussion

Author

Listed:
  • Kai Ye

    (Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan 430072, China)

  • Yangheran Piao

    (Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan 430072, China)

  • Kun Zhao

    (Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan 430072, China)

  • Xiaohui Cui

    (Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan 430072, China)

Abstract

Forecasting the prices of hogs has always been a popular field of research. Such information has played an essential role in decision-making for farmers, consumers, corporations, and governments. It is hard to predict hog prices because too many factors can influence them. Some of the factors are easy to quantify, but some are not. Capturing the characteristics behind the price data is also tricky considering their non-linear and non-stationary nature. To address these difficulties, we propose Heterogeneous Graph-enhanced LSTM (HGLTSM), which is a method that predicts weekly hog price. In this paper, we first extract the historical prices of necessary agricultural products in recent years. Then, we utilize discussions from the online professional community to build heterogeneous graphs. These graphs have rich information of both discussions and the engaged users. Finally, we construct HGLSTM to make the prediction. The experimental results demonstrate that forum discussions are beneficial to hog price prediction. Moreover, our method exhibits a better performance than existing methods.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jagris:v:11:y:2021:i:4:p:359-:d:537200
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/11/4/359/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/11/4/359/
    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. Qingfeng “Wilson” Liu, 2005. "Price relations among hog, corn, and soybean meal futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 25(5), pages 491-514, May.
    3. Hyun No Kim & Ik-Chang Choi, 2018. "The Economic Impact of Government Policy on Market Prices of Low-Fat Pork in South Korea: A Quasi-Experimental Hedonic Price Approach," Sustainability, MDPI, vol. 10(3), pages 1-16, March.
    4. Youzhu Li & Chongguang Li & Mingyang Zheng, 2014. "A Hybrid Neural Network and H-P Filter Model for Short-Term Vegetable Price Forecasting," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-10, June.
    5. Mateusz Tomal & Agata Gumieniak, 2020. "Agricultural Land Price Convergence: Evidence from Polish Provinces," Agriculture, MDPI, vol. 10(5), pages 1-20, May.
    6. Tan Ngoc Vu & Chi Minh Ho & Thang Cong Nguyen & Duc Hong Vo, 2020. "The Determinants of Risk Transmission between Oil and Agricultural Prices: An IPVAR Approach," Agriculture, MDPI, vol. 10(4), pages 1-14, April.
    7. Vasilii Erokhin, 2017. "Factors Influencing Food Markets in Developing Countries: An Approach to Assess Sustainability of the Food Supply in Russia," Sustainability, MDPI, vol. 9(8), pages 1-13, August.
    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. 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.

    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. 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.
    2. 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.
    3. Yeong Hyeon Gu & Dong Jin & Helin Yin & Ri Zheng & Xianghua Piao & Seong Joon Yoo, 2022. "Forecasting Agricultural Commodity Prices Using Dual Input Attention LSTM," Agriculture, MDPI, vol. 12(2), pages 1-18, February.
    4. Andreas Röthig & Carl Chiarella, 2007. "Investigating nonlinear speculation in cattle, corn, and hog futures markets using logistic smooth transition regression models," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 27(8), pages 719-737, August.
    5. Zhuo Chen & Bo Yan & Hanwen Kang, 2022. "Dynamic correlation between crude oil and agricultural futures markets," Review of Development Economics, Wiley Blackwell, vol. 26(3), pages 1798-1849, August.
    6. Yuanlong Ge & Holly H. Wang & Sung K. Ahn, 2010. "Cotton market integration and the impact of China's new exchange rate regime," Agricultural Economics, International Association of Agricultural Economists, vol. 41(5), pages 443-451, September.
    7. Dariusz Kusz & Bożena Kusz & Paweł Hydzik, 2022. "Changes in the Price of Food and Agricultural Raw Materials in Poland in the Context of the European Union Accession," Sustainability, MDPI, vol. 14(8), pages 1-21, April.
    8. Sun, Yunpeng & Gao, Pengpeng & Raza, Syed Ali & Shah, Nida & Sharif, Arshian, 2023. "The asymmetric effects of oil price shocks on the world food prices: Fresh evidence from quantile-on-quantile regression approach," Energy, Elsevier, vol. 270(C).
    9. Vasilii Erokhin, 2017. "Self-Sufficiency versus Security: How Trade Protectionism Challenges the Sustainability of the Food Supply in Russia," Sustainability, MDPI, vol. 9(11), pages 1-17, October.
    10. Alexander Esaulko & Vladimir Sitnikov & Elena Pismennaya & Olga Vlasova & Evgeniy Golosnoi & Alena Ozheredova & Anna Ivolga & Vasilii Erokhin, 2022. "Productivity of Winter Wheat Cultivated by Direct Seeding: Measuring the Effect of Hydrothermal Coefficient in the Arid Zone of Central Fore-Caucasus," Agriculture, MDPI, vol. 13(1), pages 1-17, December.
    11. Kun Zhang & Xing Huo & Kun Shao, 2023. "Temperature Time Series Prediction Model Based on Time Series Decomposition and Bi-LSTM Network," Mathematics, MDPI, vol. 11(9), pages 1-16, April.
    12. Emma Bruno & Rosalia Castellano & Gennaro Punzo & Luca Salvati, 2023. "Towards diverging land prices in agricultural districts? Evidence from Italy before and after the great crisis," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 69(3), pages 119-127.
    13. Zhongcheng Yan & Feng Wei & Xin Deng & Chuan Li & Yanbin Qi, 2021. "Does Land Expropriation Experience Increase Farmers’ Farmland Value Expectations? Empirical Evidence from the People’s Republic of China," Land, MDPI, vol. 10(6), pages 1-23, June.
    14. Jean Niyigaba & Daiyan Peng, 2020. "Analysis and Forecasting the Agriculture Production Sector in Rwanda," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 12(8), pages 1-91, August.
    15. El Montasser, Ghassen & Malek Belhoula, Mohamed & Charfeddine, Lanouar, 2023. "Co-explosivity versus leading effects: Evidence from crude oil and agricultural commodities," Resources Policy, Elsevier, vol. 81(C).
    16. 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.
    17. Georg Lehecka, 2015. "Do hedging and speculative pressures drive commodity prices, or the other way round?," Empirical Economics, Springer, vol. 49(2), pages 575-603, September.
    18. 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.
    19. Kryszak, Łukasz, 2020. "Income Convergence in the Agricultural Sector in the Context of the European Union’s Common Agricultural Policy," Roczniki (Annals), Polish Association of Agricultural Economists and Agribusiness - Stowarzyszenie Ekonomistow Rolnictwa e Agrobiznesu (SERiA), vol. 2020(3).
    20. Dadasaheb G. Godase & P. R. Sheshagiri Rao & Anil Gore, 2022. "Favorit: farmers volatility risk treatment," Papers 2203.12395, arXiv.org, revised Mar 2022.

    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:11:y:2021:i:4:p:359-:d:537200. 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.