학술논문
Analysis of Ship Price Prediction using Deep Learning Method
이용수 6
- 영문명
- Analysis of Ship Price Prediction using Deep Learning Method
- 발행기관
- 한국해양비즈니스학회
- 저자명
- 최정석(Jung-Suk Choi) 이정우(ChongWoo Lee)
- 간행물 정보
- 『해양비즈니스』제57호, 73~88쪽, 전체 16쪽
- 주제분류
- 경제경영 > 경영학
- 파일형태
- 발행일자
- 2023.12.31
4,720원
구매일시로부터 72시간 이내에 다운로드 가능합니다.
이 학술논문 정보는 (주)교보문고와 각 발행기관 사이에 저작물 이용 계약이 체결된 것으로, 교보문고를 통해 제공되고 있습니다.
국문 초록
영문 초록
Ships are very expensive assets, and financial uncertainties of shipping companies increase as prices change. Therefore, predicting accurate ship prices is important in terms of risk management for shipping companies. This study proposes a model for predicting ship price using deep learning method. The target of ship price prediction is capesize ships(160,000DWT), and a prediction model was constructed using monthly data from Jun 1986 to December 2022. Six models (LSTM1, LSTM2, BiLSTM1, BiLSTM2, GRU1, and GRU2) were designed for the experimental model according to the setting of the hyper parameter. Among them, each model was tested 10 times repeatedly to select the model with the lowest prediction error as the optimal model. As a result of the experiment, the GRU1 model(Avg. RMSE 9.1794, Min. RMSE 6.0019) was selected as the highest prediction accuracy among the proposed models. Through this study, the predictive excellence of the deep learning method was demonstrated, and based on this, it contributed to improving the risk management ability of ship price fluctuations.
목차
Ⅰ. Introduction
Ⅱ. Literature Reviews
Ⅲ. Proposed Algorithm
Ⅳ. Experiment Result
Ⅴ. Conclusion
References
해당간행물 수록 논문
참고문헌
교보eBook 첫 방문을 환영 합니다!
신규가입 혜택 지급이 완료 되었습니다.
바로 사용 가능한 교보e캐시 1,000원 (유효기간 7일)
지금 바로 교보eBook의 다양한 콘텐츠를 이용해 보세요!