An Empirical Study on the Effect of the Factors Influencing on the Dry Bulk Freight Rate

Bae, Sung-Hoon; Young-Mok, Ha; Park, Keunsik

doi:10.17825/klr.2018.28.5.117

Cited by 1 publication

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…해운은 크게 정기선과 부정기선으로 나눌 수 있다 (Jung, 2015). 국제 카르텔에 의해 유지되어 독․과점 형태를 이루는 정기선 해운 시장과는 달리 부정기선 해운 시장은 정해진 항로를 규 칙적으로 운항하지 않고 물품의 해상 운송 수요에 따라 이동 하는 완전 경쟁 시장 형태를 가지고 있다 (Bae and Park, 2016). 부정기선 해운 시장에서의 운임은 일반 경제 모형과 다를 바 없이 수요와 공급의 상호 작용에 의해 결정되며 이는 부정 기선의 시황과도 직결되기에 운임을 분석하고 예측하는 것은 매우 중요하다 (Chung and Kim 2015 (Mo, 2010).…”

Section: 서 론unclassified

See 1 more Smart Citation

Prediction of Dry Bulk Freight Index Using Deep Learning

Kim¹,

Hyemee²,

Sim³

et al. 2019

JKIIE

View full text Add to dashboard Cite

The market for tramp shipping is an industry characterized by volatility that is determined by the principle of perfect competition according to supply and demand. The factors affecting the supply and demand of the market are very diverse, and the mechanism of action is complicated, so forecasting in the shipping market remains a difficult task. To solve this problem, time series analysis has been conducted for a long time. However, there have been some limitations in the time series analysis which did not reflect various indicators. For this reason, most of the previous research has been stayed at an initial stage of research by comparing the prediction accuracy of two approaches : the time series analysis and the deep learning method. There has not been much research on how to improve the model performance. The purpose of this study is to improve the Baltic Dry Index(BDI) prediction performance by using Long Short Term Memory(LSTM) which is one of the deep neural networks and to consider additional variables that influence on the previous research. †

show abstract

Section: 서 론unclassified

“…(Mo and Kim, 2002). 배성훈, 박근식은 운임에 있어 영향력이 있는 변수를 상관관계를 바탕 으로 보여주었다 (Bae and Park, 2016). 운임에 영향이 있는 변 수로는 물동량, 중국 경기, 다우존스지수, 엔/달러화 환율, 국 제 유가, 국가 무역 성장률 등이 있었다 (Bakshi et al, 2011).…”

Section: 서 론unclassified

Prediction of Dry Bulk Freight Index Using Deep Learning

Kim¹,

Hyemee²,

Sim³

et al. 2019

JKIIE

View full text Add to dashboard Cite

show abstract

An Empirical Study on the Effect of the Factors Influencing on the Dry Bulk Freight Rate

Cited by 1 publication

References 0 publications

Prediction of Dry Bulk Freight Index Using Deep Learning

Prediction of Dry Bulk Freight Index Using Deep Learning

Contact Info

Product

Resources

About