Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
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A study on cabbage wholesale price forecasting model using unstructured agricultural meteorological data

비정형 농업기상자료를 활용한 배추 도매가격 예측모형 연구

  • Received : 2017.05.02
  • Accepted : 2017.05.25
  • Published : 2017.05.31
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Abstract

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.

주로 노지에서 재배되는 배추는 기상 여건에 따라 생산량의 변화가 크고, 대체 작물의 존대로 인해 가격 변동이 크게 나타난다. 기존의 연구에서는 실제 기상정보를 활용해 배추의 생산량을 예측하였으나, 본 연구에서는 실제 기상정보가 아닌 웹상의 비정형 농업기상 정보를 활용하여 도매가격을 예측하였다. 2009년 1월부터 2016년 10월까지 포털사이트에서 배추를 포함한 문서를 수집하여, 수집된 문서 내에 나타난 기상 관련 키워드를 추출하였다. 도매가격만을 이용해 자기회귀 (autoregressive; AR)모형으로 작형별 출하시기인 1, 5, 8, 11월을 예측한 단순모형과 비정형 농업기상 정보를 추가적으로 활용해 AR모형으로 예측한 농업기상모형을 비교하였다. 그 결과 비정형 농업기상 정보를 활용한 농업기상모형의 성능이 더 우수하고 예측력에 도움이 되는 것으로 나타났다.

Keywords

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