A Hybrid LSTM-Based Ensemble Learning Approach for China Coastal Bulk Coal Freight Index Prediction
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...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2021/5573650 |
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| _version_ | 1832564860848701440 |
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| author | Wei Xiao Chuan Xu Hongling Liu Xiaobo Liu |
| author_facet | Wei Xiao Chuan Xu Hongling Liu Xiaobo Liu |
| author_sort | Wei Xiao |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-fa46f69981f04430987c9d3343bc6a0a |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-fa46f69981f04430987c9d3343bc6a0a2025-02-03T01:10:08ZengWileyJournal of Advanced Transportation0197-67292042-31952021-01-01202110.1155/2021/55736505573650A Hybrid LSTM-Based Ensemble Learning Approach for China Coastal Bulk Coal Freight Index PredictionWei Xiao0Chuan Xu1Hongling Liu2Xiaobo Liu3School of Transportation and Logistics, Southwest Jiaotong University, No. 111 Erhuanlu Beiyiduan, Chengdu 610031, ChinaSchool of Transportation and Logistics, Southwest Jiaotong University, No. 111 Erhuanlu Beiyiduan, Chengdu 610031, ChinaSchool of Transportation and Logistics, Southwest Jiaotong University, No. 111 Erhuanlu Beiyiduan, Chengdu 610031, ChinaSchool of Transportation and Logistics, Southwest Jiaotong University, No. 111 Erhuanlu Beiyiduan, Chengdu 610031, ChinaChina 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.http://dx.doi.org/10.1155/2021/5573650 |
| spellingShingle | Wei Xiao Chuan Xu Hongling Liu Xiaobo Liu A Hybrid LSTM-Based Ensemble Learning Approach for China Coastal Bulk Coal Freight Index Prediction Journal of Advanced Transportation |
| title | A Hybrid LSTM-Based Ensemble Learning Approach for China Coastal Bulk Coal Freight Index Prediction |
| title_full | A Hybrid LSTM-Based Ensemble Learning Approach for China Coastal Bulk Coal Freight Index Prediction |
| title_fullStr | A Hybrid LSTM-Based Ensemble Learning Approach for China Coastal Bulk Coal Freight Index Prediction |
| title_full_unstemmed | A Hybrid LSTM-Based Ensemble Learning Approach for China Coastal Bulk Coal Freight Index Prediction |
| title_short | A Hybrid LSTM-Based Ensemble Learning Approach for China Coastal Bulk Coal Freight Index Prediction |
| title_sort | hybrid lstm based ensemble learning approach for china coastal bulk coal freight index prediction |
| url | http://dx.doi.org/10.1155/2021/5573650 |
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