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A study on cabbage wholesale price forecasting model using unstructured agricultural meteorological data
Journal of the Korean Data & Information Science Society 2017;28:617-24
Published online May 31, 2017
© 2017 Korean Data & Information Science Society.

SooHee Jang1 · Heuiju Chun2 · Inho Cho3 · DongHwan Kim4

2Department of Statistics and Information, Dongduk Women’s University
4Department of International Trade and Distribution, Anyang University
Correspondence to: Heuiju Chun
Associate professor, Department of Statistics and Information Science, Dongduk Women’s University, Seoul 02748, Korea. Email:
Received May 2, 2017; Revised May 23, 2017; Accepted May 25, 2017.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
The production of cabbage, which is mainly cultivated in open field, varies greatly depending on weather conditions, and the price fluctuation is largely due to the presence of a substitute crop. Previous studies predicted the production of cabbage using actual weather data, but in this study, we predicted the wholesale price using unstructured agricultural meteorological data on the web. From January 2009 to October 2016, we collected documents including the cabbage on the portal site, and extracted keywords related to weather in the collected documents. We compared the forecast wholesale prices of simple models and unstructured agricultural weather models at the time of shipment. The simple model is AR model using only wholesale price, and the unstructured agricultural weather model is AR model using unstructured agricultural weather data additionally. As a result, the performance of unstructured agricultural weather model was has been found to be more accurate prediction ability.
Keywords : Cabbage, wholesale price, unstructured agricultural meteorological, AR model.