Research Article
A Hybrid LSTM-Based Ensemble Learning Approach for China Coastal Bulk Coal Freight Index Prediction
Table 5
Predictive performance of hybrid LSTM-EL and benchmark models for CBCFI prediction.
| Methods | MAPE (%) | RMSE | MASE | Dsta |
| Daily CBCFI forecasting Training period: 2012/1/4∼2017/4/10 Testing period: 2019/1/7∼2020/10/13 |
| LSTM | 9.15 | 0.2419 | 1.1532 | 0.5703 | GBRT | 7.84 | 0.2342 | 0.8915 | 0.5145 | LSTM-GBRT | 6.00 | 0.1369 | 0.9132 | 0.8893 | RF | 8.62 | 0.2034 | 0.8370 | 0.5042 | LSTM-RF | 6.50 | 0.1531 | 0.9560 | 0.8539 |
| Weekly CBCFI forecasting Training period: 2012/1/9∼2017/3/24 Testing period: 2019/1/21∼2020/9/28 |
| LSTM | 9.47 | 0.1931 | 1.6532 | 0.4303 | GBRT | 8.99 | 0.1815 | 0.6173 | 0.5324 | LSTM-GBRT | 4.08 | 0.1232 | 0.9835 | 0.8034 | RF | 7.32 | 0.1673 | 0.5525 | 0.4853 | LSTM-RF | 4.43 | 0.1476 | 0.8941 | 0.8113 |
| Monthly CBCFI forecasting Training period: 2012/1/4∼2017/3/4 Testing period: 2019/4/1∼2020/10/13 |
| LSTM | 11.53 | 0.2571 | 1.0126 | 0.5863 | GBRT | 10.89 | 0.2212 | 0.7940 | 0.5932 | LSTM-GBRT | 5.04 | 0.1099 | 0.9203 | 0.9024 | RF | 11.01 | 0.2236 | 0.7864 | 0.6072 | LSTM-RF | 4.25 | 0.0943 | 0.8825 | 0.9043 |
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