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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Ga-Ae Ryu & Aziz Nasridinov & HyungChul Rah & Kwan-Hee Yoo, 2020. "Forecasts of the Amount Purchase Pork Meat by Using Structured and Unstructured Big Data," Agriculture, MDPI, vol. 10(1), pages 1-14, January.
    • Handle: RePEc:gam:jagris:v:10:y:2020:i:1:p:21-:d:310217
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    References listed on IDEAS

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    1. Xanat Vargas Meza & Han Woo Park, 2016. "Organic Products in Mexico and South Korea on Twitter," Journal of Business Ethics, Springer, vol. 135(3), pages 587-603, May.
    2. Yoo, Do-il, 2016. "Vegetable Price Prediction Using Atypical Web-Search Data," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 236211, Agricultural and Applied Economics Association.
    3. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    4. Concha Artola & Fernando Pinto & Pablo de Pedraza García, 2015. "Can internet searches forecast tourism inflows?," International Journal of Manpower, Emerald Group Publishing Limited, vol. 36(1), pages 103-116, April.
    5. Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
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    Cited by:

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    3. Manuel Muth & Michael Lingenfelder & Gerd Nufer, 2025. "The application of machine learning for demand prediction under macroeconomic volatility: a systematic literature review," Management Review Quarterly, Springer, vol. 75(3), pages 2759-2802, September.
    4. Tserenpurev Chuluunsaikhan & Jeong-Hun Kim & So-Hyun Park & Aziz Nasridinov, 2024. "Analyzing Internal and External Factors in Livestock Supply Forecasting Using Machine Learning: Sustainable Insights from South Korea," Sustainability, MDPI, vol. 16(16), pages 1-21, August.
    5. Tserenpurev Chuluunsaikhan & Ga-Ae Ryu & Kwan-Hee Yoo & HyungChul Rah & Aziz Nasridinov, 2020. "Incorporating Deep Learning and News Topic Modeling for Forecasting Pork Prices: The Case of South Korea," Agriculture, MDPI, vol. 10(11), pages 1-22, October.
    6. Ga-Ae Ryu & Tserenpurev Chuluunsaikhan & Aziz Nasridinov & HyungChul Rah & Kwan-Hee Yoo, 2023. "SCE-LSTM: Sparse Critical Event-Driven LSTM Model with Selective Memorization for Agricultural Time-Series Prediction," Agriculture, MDPI, vol. 13(11), pages 1-21, October.
    7. Wuyue An & Lin Wang & Dongfeng Zhang, 2023. "Comprehensive commodity price forecasting framework using text mining methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1865-1888, November.
    8. Gniewko Niedbała & Danuta Kurasiak-Popowska & Kinga Stuper-Szablewska & Jerzy Nawracała, 2020. "Application of Artificial Neural Networks to Analyze the Concentration of Ferulic Acid, Deoxynivalenol, and Nivalenol in Winter Wheat Grain," Agriculture, MDPI, vol. 10(4), pages 1-12, April.

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