Forecasting Agricultural Commodity Prices Using Dual Input Attention LSTM

Yeong Hyeon Gu, Dong Jin, Helin Yin, Ri Zheng, Xianghua Piao and Seong Joon Yoo
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Yeong Hyeon Gu: Department of Computer Science and Engineering, Sejong University, Seoul 05006, Korea
Dong Jin: Department of Computer Science and Engineering, Sejong University, Seoul 05006, Korea
Helin Yin: Department of Computer Science and Engineering, Sejong University, Seoul 05006, Korea
Ri Zheng: Department of Computer Science and Engineering, Sejong University, Seoul 05006, Korea
Xianghua Piao: Department of Computer Science and Engineering, Sejong University, Seoul 05006, Korea
Seong Joon Yoo: Department of Computer Science and Engineering, Sejong University, Seoul 05006, Korea

Agriculture, 2022, vol. 12, issue 2, 1-18

Abstract: Fluctuations in agricultural commodity prices affect the supply and demand of agricultural commodities and have a significant impact on consumers. Accurate prediction of agricultural commodity prices would facilitate the reduction of risk caused by price fluctuations. This paper proposes a model called the dual input attention long short-term memory (DIA-LSTM) for the efficient prediction of agricultural commodity prices. DIA-LSTM is trained using various variables that affect the price of agricultural commodities, such as meteorological data, and trading volume data, and can identify the feature correlation and temporal relationships of multivariate time series input data. Further, whereas conventional models predominantly focus on the static main production area (which is selected for each agricultural commodity beforehand based on statistical data), DIA-LSTM utilizes the dynamic main production area (which is selected based on the production of agricultural commodities in each region). To evaluate DIA-LSTM, it was applied to the monthly price prediction of cabbage and radish in the South Korean market. Using meteorological information for the dynamic main production area, it achieved 2.8% to 5.5% lower mean absolute percentage error (MAPE) than that of the conventional model that uses meteorological information for the static main production area. Furthermore, it achieved 1.41% to 4.26% lower MAPE than that of benchmark models. Thus, it provides a new idea for agricultural commodity price forecasting and has the potential to stabilize the supply and demand of agricultural products.

Keywords: agricultural commodity; attention mechanism; long short-term memory; main production area; price forecasting (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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