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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
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    References listed on IDEAS

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    1. J. Mohan Rao, 1989. "Agricultural Supply Response: A Survey," Agricultural Economics, International Association of Agricultural Economists, vol. 3(1), pages 1-22, March.
    2. Rao, J. Mohan, 1989. "Agricultural supply response: A survey," Agricultural Economics, Blackwell, vol. 3(1), pages 1-22, March.
    3. 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.
    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.
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    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. 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.
    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. 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.
    7. 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.

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