Forecasts of the Amount Purchase Pork Meat by Using Structured and Unstructured Big Data

Ga-Ae Ryu, Aziz Nasridinov, HyungChul Rah and Kwan-Hee Yoo
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Ga-Ae Ryu: Department of Computer Science, Chungbuk National University, Cheongju 28644, Korea
Aziz Nasridinov: Department of Computer Science, Chungbuk National University, Cheongju 28644, Korea
HyungChul Rah: Department of Management Information Systems, Chungbuk National University, Cheongju 28644, Korea
Kwan-Hee Yoo: Department of Computer Science, Chungbuk National University, Cheongju 28644, Korea

Agriculture, 2020, vol. 10, issue 1, 1-14

Abstract: It is believed that the huge amount of information delivered to the consumers through mass media, including television and social networks, may affect consumers’ behavior. The purpose of this study was to forecast the amount required to purchase pork belly meat by using unstructured data such as broadcast news, TV programs/shows and social network as well as structured data such as consumer panel data, retail and wholesale prices and production outputs in order to prove that mass media data release can occur ahead of actual economic activities and consumer behavior can be predicted by using these data. By using structured and unstructured data from 2010 to 2016 and five forecasting algorithms (autoregressive exogenous model and vector error correction model for time series, gradient boosting and random forest for machine learning, and long short-term memory for recurrent neural network), the amounts required to purchase pork belly meat in 2017 were forecasted and compared with the actual amounts to validate model accuracy. Our findings suggest that when unstructured data were combined with structured data, the forecast pattern is improved. To date, our study is the first report that forecasts the demand of pork meat by using structured and unstructured data.

Keywords: agri-food; purchase forecast; unstructured big data; social network service; pork meat (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: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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