인공지능을 이용한 주요 수산물 가격 예측 모형 비교에 관한 연구

• 영문명: Comparative Study on Price Forecasting Models of Major Fisheries Products Using Artificial Intelligence
• 발행기관: 한국해양비즈니스학회
• 저자명: 박철형(Cheol-Hyung Park)
• 간행물 정보: 『해양비즈니스』제59호, 25~44쪽, 전체 20쪽
• 주제분류: 경제경영 > 경영학
• 파일형태: PDF
• 발행일자: 2024.08.31

국문 초록

The purpose of this study is to establish a model for predicting the fluctuations of the frozen wholesale market prices of five major consumption fish species such as mackerel, hairtail, pollock, squid, and yellow corvina using five AI machine learning algorithms such as Decision Tree, Random Forest, Gradient-Boost, XG-Boost, and SVM, and to compare the predictive powers with each other using various forecasting indicators. The case of best prediction power was the prediction of the price of hairtail using a random forest, where the accuracy was 0.923, even more showing 100% precision, especially in the case of price decline. Among the five algorithms, the highest predictive power was SVM, with an average accuracy at 0.683, while the lowest one was XG-Boost, with an average accuracy at 0.614. When comparing the predictive powers of the algorithm for each individual fish species, Gradient-Boost and SVM were the best for mackerel, decision tree and random forest for hairtail, and random forest and XG-Boost for pollack. In addition, the decision trees was found to be the algorithms with the highest predictive power for squid, just like SVM was for yellow corvina.

목차

Ⅰ. 서론

Ⅱ. 머신러닝 알고리즘 및 평가지표

Ⅳ. 모형의 예측 성능 평가

Ⅴ. 결론

참고문헌

키워드

인공지능 기계학습 랜덤포레스트 AI machine learning Random Forest XG-Boost SVM

참고문헌

• 산업혁신연구
• 해양비즈니스
• 해양비즈니스
• Corrosion Seience and Technology
• 통신정보 합동학술대회
• 수산경영론집
• 대한원격탐사학회지
• 수산해양교육연구
• Neural Computation
• Machine Learning
• Knowledge Discovery and Data Mining
• Machine Learning
• European Journal of Social Sciences
• The Annals of Statistics
• Morgan Kaufmann Publishers Inc