Research Article
Meta-Learning Enhanced Trade Forecasting: A Neural Framework Leveraging Efficient Multicommodity STL Decomposition
Table 3
Performance comparison of various models for imported commodity value series.
| Model | MAE | RMSE | MAPE |
| LastValuePredictor | 3.01 | 5.25 | 0.2011 | ARIMA | 2.71 | 3.23 | 0.1921 | VAR | 10.18 | 11.79 | 0.6878 | Bagging | 2.97 | 2.39 | 0.1555 | LSTM | 2.31 | 3.70 | 0.1874 | GRU | 3.02 | 5.10 | 0.2272 | DeepAR | 2.62 | 4.38 | 0.2020 | DeepVAR | 2.96 | 5.26 | 0.2063 | N-Beats | 2.04 | 3.54 | 0.1705 | N-Hits | 1.94 | 3.43 | 0.1641 | TFT | 2.07 | 3.80 | 0.1628 | TFSTL | | | | Meta-TFSTL (fine-tuning) | | | |
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Bold: best; underline: second best; italics: best in baseline.
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