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A Baltic Dry Index Prediction using Deep Learning Models

Title
A Baltic Dry Index Prediction using Deep Learning Models
Author
이건우
Keywords
Artificial Neural Network; Baltic Dry Index; Big Data; Long Short-Term memory; Recurrent Neural network
Issue Date
2021-06
Publisher
KOREA TRADE RESEARCH ASSOC
Citation
JOURNAL OF KOREA TRADE, v. 25, no. 4, page. 17-36
Abstract
Purpose - This study provides useful information to stakeholders by forecasting the tramp shipping market, which is a completely competitive market and has a huge fluctuation in freight rates due to low barriers to entry. Moreover, this study provides the most effective parameters for Baltic Dry Index (BDI) prediction and an optimal model by analyzing and comparing deep learning models such as the artificial neural network (ANN), recurrent neural network (RNN), and long short-term memory (LSTM). Design/methodology - This study uses various data models based on big data. The deep learning models considered are specialized for time series models. This study includes three perspectives to verify useful models in time series data by comparing prediction accuracy according to the selection of external variables and comparison between models. Findings - The BDI research reflecting the latest trends since 2015, using weekly data from 1995 to 2019 (25 years), is employed in this study. Additionally, we tried finding the best combination of BDI forecasts through the input of external factors such as supply, demand, raw materials, and economic aspects. Moreover, the combination of various unpredictable external variables and the fundamentals of supply and demand have sought to increase BDI prediction accuracy. Originality/value - Unlike previous studies, BDI forecasts reflect the latest stabilizing trends since 2015. Additionally, we look at the variation of the model's predictive accuracy according to the input of statistically validated variables. Moreover, we want to find the optimal model that minimizes the error value according to the parameter adjustment in the ANN model. Thus, this study helps future shipping stakeholders make decisions through BDI forecasts.
URI
https://jcr.clarivate.com/jcr-jp/journal-profile?journal=J%20KOREA%20TRADE&year=2020https://repository.hanyang.ac.kr/handle/20.500.11754/166556
ISSN
1229-828X
Appears in Collections:
COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > TRANSPORTATION AND LOGISTICS ENGINEERING(교통·물류공학과) > Articles
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