Affiliation:
1. School of Transportation and Logistics, Southwest Jiaotong University, No. 111 Erhuanlu Beiyiduan, Chengdu 610031, China
2. National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University, Chengdu, Sichuan, China
3. National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Chengdu 611756, Sichuan, China
Abstract
China Coastal Bulk Coal Freight Index (CBCFI) reflects how the coastal coal transporting market’s freight rates in China are fluctuated, significantly impacting the enterprise’s strategic decisions and risk-avoiding. Though trend analysis on freight rate has been extensively conducted, the property of the shipping market, i.e., it varies over time and is not stable, causes CBCFI to be hard to be accurately predicted. A novel hybrid approach is developed in the paper, integrating Long Short-Term Memory (LSTM) and ensemble learning techniques to forecast CBCFI. The hybrid LSTM-based ensemble learning (LSTM-EL) approach predicts the CBCFI by extracting the time-dependent information in the original data and incorporating CBCFI-related data, e.g., domestic and overseas thermal coal spot prices, coal inventory, the prices of fuel oil, and crude oil. To demonstrate the applicability and generality of the proposed approach, different time-scale datasets (e.g., daily, weekly, and monthly) in a rolling forecasting experiment are conducted. Empirical results show that domestic and overseas thermal coal spot prices and crude oil prices have great influences on daily, weekly, and monthly CBCFI values. And in daily, weekly, and monthly forecasting cases, the LSMT-EL approaches have higher prediction accuracy and a greater trend complying ratio than the relevant single ensemble learning algorithm. The hybrid method outperforms others when it works with information involving a dramatic market recession, elucidating CBCFI’s predictable ability. The present work is of high significance to general commerce, commerce-related, and hedging strategic procedures within the coastal shipping market.
Funder
Ministry of Science and Technology of the People's Republic of China
Subject
Strategy and Management,Computer Science Applications,Mechanical Engineering,Economics and Econometrics,Automotive Engineering
Cited by
7 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献