Journal of the Korea Convergence Society (한국융합학회논문지)

Korea Convergence Society (한국융합학회)
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Design of e-commerce business model through AI price prediction of agricultural products

농산물 AI 가격 예측을 통한 전자거래 비즈니스 모델 설계

  • Han, Nam-Gyu (Dept. of Computer Science, Washington State University) ;
  • Kim, Bong-Hyun (Dept. of Computer Engineering, Seowon University)
  • 한남규 (워싱턴주립대학교 컴퓨터과학과) ;
  • 김봉현 (서원대학교 컴퓨터공학과)
  • Received : 2021.11.05
  • Accepted : 2021.12.20
  • Published : 2021.12.28
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Abstract

For agricultural products, supply is irregular due to changes in meteorological conditions, and it has high price elasticity. For example, if the supply decreases by 10%, the price increases by 50%. Due to these fluctuations in the prices of agricultural products, the Korean government guarantees the safety of prices to producers through small merchants' auctions. However, when prices plummet due to overproduction, protection measures for producers are insufficient. Therefore, in this paper, we designed a business model that can be used in the electronic transaction system by predicting the price of agricultural products with an artificial intelligence algorithm. To this end, the trained model with the training pattern pairs and a predictive model was designed by applying ARIMA, SARIMA, RNN, and CNN. Finally, the agricultural product forecast price data was classified into short-term forecast and medium-term forecast and verified. As a result of verification, based on 2018 data, the actual price and predicted price showed an accuracy of 91.08%.

농산물은 기상, 기후 등의 변화로 인해 공급이 불규칙하고, 공급량이 10% 하락하면 가격이 50% 상승하는 가격 탄력성이 매우 높다. 이러한 농산물 가격의 변동으로 인해 소상인의 경매를 통해 생산자에게 대금의 안전성을 보장하고 있다. 그러나, 과잉생산으로 가격이 폭락할 경우, 생산자에 대한 보호 조치는 미비한 실정이다. 따라서, 본 논문에서는 농산물에 대한 가격을 인공지능 알고리즘으로 예측하여 전자거래 시스템에 활용할 수 있는 비즈니스 모델을 설계하였다. 이를 위해, 학습 패턴 쌍으로 모델을 학습시키고, ARIMA, SARIMA, RNN, CNN을 적용하여 예측 모델을 설계하였다. 최종적으로, 농산물 예측가격 데이터를 단기예측과 중기예측으로 분류하여 검증하였다. 검증 결과, 2018년 데이터를 기반으로 실제 가격과 예측 가격이 91.08%의 정확도를 나타냈다.

Keywords

References

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