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A Prediction Model for Agricultural Products Price with LSTM Network
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LSTM 네트워크를 활용한 농산물 가격 예측 모델

논문 기본 정보

Type
Academic journal
Author
Sungho Shin (한국과학기술정보연구원) Mikyoung Lee (한국과학기술정보연구원) Sa-kwang Song (한국과학기술정보연구원)
Journal
The Korea Contents Society JOURNAL OF THE KOREA CONTENTS ASSOCIATION Vol.18 No.11 KCI Accredited Journals
Published
2018.11
Pages
416 - 429 (14page)

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A Prediction Model for Agricultural Products Price with LSTM Network
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Typhoons and floods are natural disasters that occur frequently, and the damage resulting from these disasters must be in advance predicted to establish appropriate responses. Direct damages such as building collapse, human casualties, and loss of farms and fields have more attention from people than indirect damages such as increase of consumer prices. But indirect damages also need to be considered for living. The agricultural products are typical consumer items affected by typhoons and floods. Sudden, powerful typhoons are mostly accompanied by heavy rains and damage agricultural products; this increases the retail price of such products. This study analyzes the influence of natural disasters on the price of agricultural products by using a deep learning algorithm. We decided rice, onion, green onion, spinach, and zucchini as target agricultural products, and used data on variables that influence the price of agricultural products to create a model that predicts the price of agricultural products. The result shows that the model’s accuracy was about 0.069 measured by RMSE, which means that it could explain the changes in agricultural product prices. The accurate prediction on the price of agricultural products can be utilized by the government to respond natural disasters by controling amount of supplying agricultural products.
#자연재해 #농산물 #가격 #예측 #딥러닝 #LSTM #RMSE #날씨 #Natural Disaster #Agricultural Product #Price #Prediction #Deep Learning #Weather

Contents

요약
Abstract
Ⅰ. 서론
Ⅱ. 관련 연구
Ⅲ. 연구 설계
Ⅳ. 학습 데이터 구축
Ⅴ. 실험 및 결과
Ⅵ. 결론
참고문헌

References (16)

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[학술지(정기간행물)] - M. Fafchamps / 2012 / Impact of SMS -Based Agricultural Information on Indian Farmers / the World Bank Economic Review 26 (3) : 383 ~ 414 google schola [학술지(정기간행물)] - A. M. Rather / 2015 / Recurrent Neural Network and a Hybrid Model for Prediction of Stock Returns / Expert Systems with Applications 42 : 3234 ~ 3241 google schola [학술지(정기간행물)] - H. Chiroma / 2015 / Evolutionary Neural Network Model for West Texas Intermediate Crude Oil Price Prediction / Applied Energy 142 : 266 ~ 273 google schola [학술지(정기간행물)] - J. H. Zhang / 2014 / Tomato Prices Time Series Prediction Model Based on Wavelet Neural Network / Applied Mechanics &Materials 644-650 : 2636 ~ 2640 google schola [학술대회논문] - C. Wang / 2013 / Design and Implementation of Agricultural Product Prices Short-Term Forecasting System / Proceedings of 2013 World Agricultural Outlook Conference : 15 ~ 27 google schola

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UCI(KEPA) : I410-ECN-0101-2019-310-000211738