Agricultural Price Prediction Based on Combined Forecasting Model under Spatial-Temporal Influencing Factors

Yan Guo, Dezhao Tang, Wei Tang, Senqi Yang, Qichao Tang, Yang Feng and Fang Zhang ()
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Yan Guo: College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China
Dezhao Tang: College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China
Wei Tang: College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China
Senqi Yang: College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China
Qichao Tang: College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China
Yang Feng: College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China
Fang Zhang: College of Information Engineering, Sichuan Agricultural University, Ya’an 625000, China

Sustainability, 2022, vol. 14, issue 17, 1-18

Abstract: Grain product price fluctuations affect the input of production factors and impact national food security. Under the influence of complex factors, such as spatial-temporal influencing factors, price correlation, and market diversity, it is increasingly important to improve the accuracy of grain product price prediction for agricultural sustainable development. Therefore, successful prediction of the agricultural product plays a vital role in the government’s market regulation and the stability of national food security. In this paper, the price of corn in Sichuan Province is taken as an example. Firstly, the apriori algorithm was used to search for the spatial-temporal influencing factors of price changes. Secondly, the Attention Mechanism Algorithm, Long Short-term Memory (LSTM), Autoregressive Integrated Moving Average (ARIMA), and Back Propagation (BP) Neural Network models were combined into the AttLSTM-ARIMA-BP model to predict the accurate price. Compared with the other seven models, the AttLSTM-ARIMA-BP model achieves the best prediction effect and possesses the strongest robustness, which improves the accuracy of price forecasting in complex environments and makes the application to other fields possible.

Keywords: agricultural development; apriori algorithm; combination model; food security; price fluctuation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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