Table 6 Result of Semi-supervised Model.
From: Input driven optimization of echo state network parameters for prediction on chaotic time series
Dataset | Metric | Simple model | DPP approach | IPP approach | |
|---|---|---|---|---|---|
A1 | NRMSE | 3.49 ± 9.1 | 0.1105 ± 2.1e-02 | 0.1804 ± 1.2e-02 | |
MSE | 11.13 ± 60 | 0.01116 ± 0.002 | 0.02976 ± 0.006 | ||
A2 | NRMSE | 5.1 ± 9.8 | 0.1522 ± 1e-02 | 0.1603 ± 1.1e-02 | |
MSE | 23.78 ± 19 | 0.02118 ± 0.002 | 0.0235 ± 0.003 | ||
B1 | NRMSE | 8.37 ± 9.7 | 0.1489 ± 0.07 | 0.067 ± 0.15 | |
MSE | 64.07 ± 40 | 0.0202 ± 0.01 | 0.0041 ± 0.01 | ||
B2 | NRMSE | 0.488 ± 0.0643 | 0.1780 ± 0.08 | 0.1259 ± 0.01 | |
MSE | 0.2177 ± 0.06 | 0.0289 ± 0.02 | 0.0144 ± 0.002 | ||
C1 | NRMSE | 8.37 ± 9.7 | 0.0012 ± 1e-04 | 0.0008 ± 1e-03 | |
MSE | 64.07 ± 50 | (1.32 ± 0.23)e-5 | (1.1 ± 2)e-5 | ||
C2 | NRMSE | 0.02 ± 0.03 | 0.0009 ± 2e-04 | 0.0006 ± 1e-04 | |
MSE | 0.0003 ± 0.001 | (2.4 ± 0.2)e-5 | (1.1 ± 0.7)e-5 | ||
D1 | NRMSE | 8.37 ± 9.7 | 0.1422 ± 0.01 | 0.0712 ± 0.05 | |
MSE | 64.07 ± 35 | 0.01849 ± 0.002 | 0.004636 ± 0.006 | ||
D2 | NRMSE | 0.2619 ± 0.005 | 0.1565 ± 0.05 | 0.0947 ± 0.03 | |
MSE | 0.062731 ± 0.002 | 0.0224 ± 0.01 | 0.0082 ± 0.005 | ||
E1 | NRMSE | 0.4105 ± 0.01 | 0.3459 ± 0.02 | 0.3443 ± 0.01 | |
MSE | 0.1541 ± 0.006 | 0.0613 ± 0.007 | 0.0607 ± 0.004 | ||
E2 | NRMSE | 0.4221 ± 0.01 | 0.3435 ± 0.02 | 0.3411 ± 0.03 | |
MSE | 0.1629 ± 0.008 | 0.0604 ± 0.007 | 0.05964 ± 0.01 | ||
F1 | NRMSE | 0.4041 ± 0.01 | 0.3463 ± 0.02 | 0.2873 ± 0.1 | |
MSE | 0.1493 ± 0.007 | 0.0614 ± 0.007 | 0.0457 ± 0.05 | ||
F2 | NRMSE | 0.4035 ± 0.04 | 0.3440 ± 0.02 | 0.3383 ± 0.03 | |
MSE | 0.1489 ± 0.003 | 0.0606 ± 0.007 | 0.0581 ± 0.01 | ||
