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Vegetable Price Prediction Using Atypical Web-Search Data

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  • Yoo, Do-il

Abstract

Our study focuses on 3 vegetables mainly purchased in Korea; onion, garlic, and dried red pepper. We develop atypical index reflecting consumers’ attention on those vegetables from social network service (SNS) websites and major portal sites such as Google. Specifically, using text mining program, we gather associate web-search data, making simple query data measuring frequency on websites and Term Frequency – Inverse Document Frequency (TF-IDF) considering weights of core keywords on websites. We introduce those asymptotic indexes into the Bayesian structural time series models with climate factors impacting vegetable prices. Results show that the introduction of atypical web-search data can improve vegetable price prediction power compared to pure time-series models without atypical indexes.

Suggested Citation

  • Yoo, Do-il, "undated". "Vegetable Price Prediction Using Atypical Web-Search Data," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 236211, Agricultural and Applied Economics Association.
    • Handle: RePEc:ags:aaea16:236211
    DOI: 10.22004/ag.econ.236211
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    References listed on IDEAS

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    1. 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.
    2. J. Durbin, 2002. "A simple and efficient simulation smoother for state space time series analysis," Biometrika, Biometrika Trust, vol. 89(3), pages 603-616, August.
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    Cited by:

    1. 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.
    2. 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.

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