A Hybrid LSTM-Based Ensemble Learning Approach for China Coastal Bulk Coal Freight Index Prediction

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.

Forecasting shipping index using CEEMD-PSO-BiLSTM model

“…In the study of time series prediction, many papers have shown that LSTM has obvious advantages.Kim et al [ 24 ] found that LSTM can effectively improve the prediction performance of BDI by comparing time series analysis and deep learning methods. In a study by Xiao et al [ 25 ], they combined LSTM and integrated learning techniques to forecast CBCFI, which outperformed other methods when dealing with information involving dramatic market downturns. In stock market research, Nelson et al [ 26 ] proposed a stock price prediction model based on LSTM, which was used to simulate transactions and compared with the benchmark model to evaluate its prediction performance.…”

Practicality of Green H2 Economy for Industry and Maritime Sector Decarbonization through Multiobjective Optimization and RNN-LSTM Model Analysis

“…The size of the facility depends on knowing the green H 2 demand beforehand, and this demand is directly correlated with the freight rates, and the vessels’ dead weight ton (DWT). However, freight rates are uncertain and unstable over time, mainly due to variation in the property of the shipping market, geopolitical situation, weather conditions, global supply demand, etc. Increasing literature proposes multistep prediction approaches to forecast freight rates using time series models, mainly including the Auto-Regressive model (AR), the Auto-Regressive Integrated Moving Average model (ARIMA), and their variants .…”

China Coastal Bulk (Coal) Freight Index Forecasting Based on an Integrated Model Combining ARMA, GM and BP Model Optimized by GA

“…So, it can, to a certain extent, reflect the economic development of China and the trend of coastal bulk trade. The release of CBCFI helps the development of the shipping index system in the China coastal coal transportation market [4]. As the "barometer" of the coastal coal transportation market, the index can accurately and timely reflect the dramatic and frequent price fluctuations in the coastal coal transportation market [5].…”