The butterfly effect in the price of agricultural products: A multidimensional spatial-temporal association mining

Yan Guo, Xiaonan Hu, Zepeng Wang, Wei Tang, Deyu Liu, Yunzhong Luo and Hongxiang Xu
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Yan Guo: College of Information and Engineering, Sichuan Agricultural University, Yaan, China
Xiaonan Hu: College of Information and Engineering, Sichuan Agricultural University, Yaan, China
Zepeng Wang: College of Information and Engineering, Sichuan Agricultural University, Yaan, China
Wei Tang: College of Information and Engineering, Sichuan Agricultural University, Yaan, China
Deyu Liu: College of Information and Engineering, Sichuan Agricultural University, Yaan, China
Yunzhong Luo: College of Information and Engineering, Sichuan Agricultural University, Yaan, China
Hongxiang Xu: College of Information and Engineering, Sichuan Agricultural University, Yaan, China

Agricultural Economics, 2021, vol. 67, issue 11, 457-467

Abstract: With the advent of the era of big data, data mining methods show their powerful information mining ability in various fields, seeking the association information hidden in the data, which is convenient for people to make scientific decisions. This paper analyses the butterfly effect in the agricultural product industry chain from the perspective of producer and consumer by using multidimensional time and space theory and proposes a new price forecasting method. We consider that the price change of agricultural products is not only affected by the balance of market supply and demand but also by the factors of time and space. Taking the pig industry chain of Sichuan Province as an example, this paper explores and excavates the data from 2010 to 2020 in the time dimension. Interestingly, we found that the price changes in pork in the market are generally highly correlated with the prices of slaughtered pigs, piglets a few weeks ago and the prices of multiple feed a few months ago. Based on the precise time-space factors, we improved the price forecasting model, greatly improved the accuracy of price prediction, and proved the effectiveness of multidimensional spatiotemporal association mining. The research in this paper is helpful to establish a brand-new agricultural product price prediction theory, which is of great significance to the development of the agricultural economy and global poverty alleviation.

Keywords: agricultural economics; data mining; industrial chain; machine learning; price forecast (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:caa:jnlage:v:67:y:2021:i:11:id:128-2021-agricecon

DOI: 10.17221/128/2021-AGRICECON

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