LSTM을 이용한 돼지고기 가격 예측 연구

• 영문명: A Study on Pork Price Prediction Using LSTM
• 발행기관: 한국농식품정책학회
• 저자명: 김동현(Kim, Dong Hyun) 김수현(Kim, Su Hyeon) 유찬주(Yu, Chan Ju)
• 간행물 정보: 『농업경영.정책연구』농업경영·정책연구 48권 4호, 593~612쪽, 전체 20쪽
• 주제분류: 농수해양 > 식품과학
• 파일형태: PDF
• 발행일자: 2021.12.31

영문 초록

In this study, we propose LSTM, a technique for predicting pork prices to stabilize the supply and demand of pork. The LSTM model is trained using the attributes of day and month as additional inputs to historical time series data. The performance of the proposed LSTM model was compared to that of ARIMA, SARIMA, autoARIMA, and standard LSTM in terms of three performance metrics: mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE). As a result of the experiment, the error rate of the proposed LSTM model was significantly lower than that of other existing methods. In future research, it will be necessary to redefine the analysis of causal relationships such as livestock substitutes and supplements, and to conduct practical studies such as linking OpenAPI with big data of public institutions to improve the accuracy of price prediction.

목차

Ⅰ. 서 론

Ⅱ. 이론적 배경 및 선행연구

Ⅲ. 모형 수립

Ⅳ. 예측결과

Ⅴ. 결 론

참고문헌

키워드

Time series Prediction Deep-Learning ARIMA SARIMA autoARIMA LSTM