IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v10y2020i12p612-d458798.html
   My bibliography  Save this article

STL-ATTLSTM: Vegetable Price Forecasting Using STL and Attention Mechanism-Based LSTM

Author

Listed:
  • Helin Yin

    (Department of Computer Science and Engineering, Sejong University, Seoul 05006, Korea)

  • Dong Jin

    (Department of Computer Science and Engineering, Sejong University, Seoul 05006, Korea)

  • Yeong Hyeon Gu

    (Department of Computer Science and Engineering, Sejong University, Seoul 05006, Korea)

  • Chang Jin Park

    (Department of Bioresources Engineering, Sejong University, Seoul 05006, Korea)

  • Sang Keun Han

    (Supply & Demand Management Office, Integrated Information System Team, Korea Agro-Fisheries & Food Trade Corporation, Naju 58326, Korea)

  • Seong Joon Yoo

    (Department of Computer Science and Engineering, Sejong University, Seoul 05006, Korea)

Abstract

It is difficult to forecast vegetable prices because they are affected by numerous factors, such as weather and crop production, and the time-series data have strong non-linear and non-stationary characteristics. To address these issues, we propose the STL-ATTLSTM (STL-Attention-based LSTM) model, which integrates the seasonal trend decomposition using the Loess (STL) preprocessing method and attention mechanism based on long short-term memory (LSTM). The proposed STL-ATTLSTM forecasts monthly vegetable prices using various types of information, such as vegetable prices, weather information of the main production areas, and market trading volumes. The STL method decomposes time-series vegetable price data into trend, seasonality, and remainder components. It uses the remainder component by removing the trend and seasonality components. In the model training process, attention weights are assigned to all input variables; thus, the model’s prediction performance is improved by focusing on the variables that affect the prediction results. The proposed STL-ATTLSTM was applied to five crops, namely cabbage, radish, onion, hot pepper, and garlic, and its performance was compared to three benchmark models (i.e., LSTM, attention LSTM, and STL-LSTM). The performance results show that the LSTM model combined with the STL method (STL-LSTM) achieved a 12% higher prediction accuracy than the attention LSTM model that did not use the STL method and solved the prediction lag arising from high seasonality. The attention LSTM model improved the prediction accuracy by approximately 4% to 5% compared to the LSTM model. The STL-ATTLSTM model achieved the best performance, with an average root mean square error (RMSE) of 380, and an average mean absolute percentage error (MAPE) of 7%.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jagris:v:10:y:2020:i:12:p:612-:d:458798
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/10/12/612/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2077-0472/10/12/612/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Marcel Fafchamps & Bart Minten, 2012. "Impact of SMS-Based Agricultural Information on Indian Farmers," The World Bank Economic Review, World Bank, vol. 26(3), pages 383-414.
    2. J. Mohan Rao, 1989. "Agricultural Supply Response: A Survey," Agricultural Economics, International Association of Agricultural Economists, vol. 3(1), pages 1-22, March.
    3. 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.
    4. Rao, J. Mohan, 1989. "Agricultural supply response: A survey," Agricultural Economics, Blackwell, vol. 3(1), pages 1-22, March.
    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. 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. 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.
    3. Dadasaheb G. Godase & P. R. Sheshagiri Rao & Anil Gore, 2022. "Favorit: farmers volatility risk treatment," Papers 2203.12395, arXiv.org, revised Mar 2022.
    4. Emmanuel Ekene Okere & Vipin Balyan, 2025. "A Deep Learning-Based Prediction and Forecasting of Tomato Prices for the Cape Town Fresh Produce Market: A Model Comparative Analysis," Forecasting, MDPI, vol. 7(2), pages 1-18, May.
    5. 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.
    6. 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.
    7. Xiaojie Xu & Yun Zhang, 2023. "Steel price index forecasting through neural networks: the composite index, long products, flat products, and rolled products," Mineral Economics, Springer;Raw Materials Group (RMG);Luleå University of Technology, vol. 36(4), pages 563-582, December.
    8. Nikita V. Martyushev & Vladislav Spitsin & Roman V. Klyuev & Lubov Spitsina & Vladimir Yu. Konyukhov & Tatiana A. Oparina & Aleksandr E. Boltrushevich, 2025. "Predicting Firm’s Performance Based on Panel Data: Using Hybrid Methods to Improve Forecast Accuracy," Mathematics, MDPI, vol. 13(8), pages 1-33, April.
    9. 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.
    10. 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. Nehring, Richard F., 1991. "Output and Input Subsidy Policy Options in Bangladesh," Journal of Agricultural Economics Research, United States Department of Agriculture, Economic Research Service, vol. 43(02), pages 1-13.
    2. Vavra, Pavel & Colman, David, 2003. "The analysis of UK crop allocation at the farm level: implications for supply response analysis," Agricultural Systems, Elsevier, vol. 76(2), pages 697-713, May.
    3. Christophe Gouel, 2013. "Rules versus Discretion in Food Storage Policies," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 95(4), pages 1029-1044.
    4. Eric B. Schneider, 2014. "Prices and production: agricultural supply response in fourteenth-century England," Economic History Review, Economic History Society, vol. 67(1), pages 66-91, February.
    5. Gouel, Christophe & Laborde, David, 2021. "The crucial role of domestic and international market-mediated adaptation to climate change," Journal of Environmental Economics and Management, Elsevier, vol. 106(C).
    6. Chr. R. Weiss, 1992. "The Effect Of Price Reduction And Direct Income Support Policies On Agricultural Input Markets In Austria," Journal of Agricultural Economics, Wiley Blackwell, vol. 43(1), pages 1-13, January.
    7. Hareau, Guy G. & Mills, Bradford F. & Norton, George W., 2006. "The potential benefits of herbicide-resistant transgenic rice in Uruguay: Lessons for small developing countries," Food Policy, Elsevier, vol. 31(2), pages 162-179, April.
    8. Koffi-Tessio, Egnonto, 2000. "Incitations et offre du coton au Togo. Une estimation économétrique," Économie rurale, French Society of Rural Economics (SFER Société Française d'Economie Rurale), vol. 257.
    9. repec:ilo:ilowps:300473 is not listed on IDEAS
    10. Md Zabid Iqbal & Bruce A. Babcock, 2018. "Global growing‐area elasticities of key agricultural crops estimated using dynamic heterogeneous panel methods," Agricultural Economics, International Association of Agricultural Economists, vol. 49(6), pages 681-690, November.
    11. Donato, Romano & Carraro, Alessandro, 2015. "Modelling Acreage, Production and Yield Supply Response to Domestic Price Volatility," 2015 Fourth Congress, June 11-12, 2015, Ancona, Italy 207278, Italian Association of Agricultural and Applied Economics (AIEAA).
    12. Magrini, Emiliano & Morales-Opazo, Cristian & Balie, Jean, 2014. "Supply response along the value chain in selected SSA countries: the case of grains," 2014: Food, Resources and Conflict, December 7-9, 2014. San Diego, California 197193, International Agricultural Trade Research Consortium.
    13. World Bank, 1991. "World Development Report 1991," World Bank Publications - Books, The World Bank Group, number 5974, April.
    14. Perrings C., 1994. "Sustainable livelihoods and environmentally sound technology," ILO Working Papers 993004733402676, International Labour Organization.
    15. Kamau, Mercy & Mills, Bradford F., 1998. "Technology, location and trade: Kenyan vegetables," Agricultural Systems, Elsevier, vol. 58(3), pages 395-415, November.
    16. Jouf, C. & Lawson, L.A., 2022. "European farmers’ responses to higher commodity prices: Cropland expansion or forestlands preservation?," Ecological Economics, Elsevier, vol. 191(C).
    17. Bhanupong Nidhiprabha, 2019. "Commodity Price Cycles, the Agricultural Trap, and Thailand's Incessant Subsidies," Asian Economic Papers, MIT Press, vol. 18(2), pages 49-69, Summer.
    18. Mofya-Mukuka, Rhoda & Abdulai, Awudu, 2012. "Supply Response of Export Crops in Zambia: The Case of Coffee," Food Security Collaborative Policy Briefs 123556, Michigan State University, Department of Agricultural, Food, and Resource Economics.
    19. Roman Keeney & Thomas W. Hertel, 2008. "U.S. Market Potential For Dried Distillers Grain With Solubles," Working Papers 08-13, Purdue University, College of Agriculture, Department of Agricultural Economics.
    20. Kergna, Alpha & Smale, Melinda & Assima, Amidou & Diallo, Abdoulaye & Weltzien, Eva & Rattunde, Fred, 2017. "The potential economic impact of Guinea-race sorghum hybrids in Mali: A comparison of research and development paradigms," African Journal of Agricultural and Resource Economics, African Association of Agricultural Economists, vol. 12(01), March.
    21. Huang, Jikun & Rozelle, Scott, 1996. "Technological change: Rediscovering the engine of productivity growth in China's rural economy," Journal of Development Economics, Elsevier, vol. 49(2), pages 337-369, May.

    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:10:y:2020:i:12:p:612-:d:458798. 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.