Research Article
Short-Term Speed Prediction Using Remote Microwave Sensor Data: Machine Learning versus Statistical Model
Table 3
Prediction accuracy of models for different forecasting steps ahead in station C.
| MAE | Number of forecasting steps ahead | 1 | 3 | 5 | 10 |
| BPNN | 2.5694 | 3.4613 | 4.1736 | 5.6407 | NARXNN | 2.5484 | 3.4757 | 4.1458 | 5.7245 | SVM-RBF | 2.6617 | 3.6111 | 4.2583 | 5.6255 | SVM-LIN | 2.8896 | 3.7087 | 4.3468 | 5.6221 | MLR | 3.9650 | 3.8713 | 4.5163 | 5.9062 | ARIMA | 2.6675 | 3.6235 | 4.2857 | 5.6745 | VAR | 2.6611 | 3.6798 | 4.3051 | 5.4380 | ST | 2.6359 | 3.5298 | 4.0963 | 5.2024 |
| MAPE (%) | Number of forecasting steps ahead | 1 | 3 | 5 | 10 |
| BPNN | 6.5606 | 8.9994 | 11.3564 | 16.3149 | NARXNN | 6.4875 | 9.1176 | 11.2402 | 16.6231 | SVM-RBF | 6.7398 | 9.3483 | 11.5343 | 16.4376 | SVM-LIN | 7.0707 | 10.1794 | 10.9789 | 15.5072 | MLR | 7.1717 | 10.4244 | 11.3601 | 16.7207 | ARIMA | 6.7822 | 9.4230 | 11.6734 | 16.7901 | VAR | 6.6898 | 9.1819 | 11.0211 | 14.5708 | ST | 6.6709 | 8.9993 | 10.7423 | 14.3994 |
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