Research Article
Meta-Learning Enhanced Trade Forecasting: A Neural Framework Leveraging Efficient Multicommodity STL Decomposition
Table 4
Performance comparison of various models for exported commodity value series.
| Model | MAE | RMSE | MAPE |
| LastValuePredictor | 3.50 | 7.36 | 0.1854 | ARIMA | 2.64 | 5.29 | 0.1811 | VAR | 7.15 | 8.13 | 0.3802 | Bagging | 3.63 | 5.14 | 0.1983 | LSTM | 3.70 | 7.18 | 0.2064 | GRU | 3.82 | 8.15 | 0.2344 | DeepAR | 3.00 | 6.60 | 0.2210 | DeepVAR | 2.65 | 5.96 | 0.2185 | N-Beats | 2.92 | 6.08 | 0.1763 | N-Hits | 3.04 | 6.77 | 0.1804 | TFT | 2.76 | 4.89 | 0.1781 | TFSTL | | | | Meta-TFSTL (fine-tuning) | | | |
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Bold: best; underline: second best; italics: best in baseline.
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