Table 6.
The monthly root mean square relative error (RRMSE) for the classical machine learning models.
| Model | JAN | FEB | MAR | APR | MAY | JUN | JUL | AUG | SEP | OCT | NOV | DEC |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| MLP | 36.286 | 32.22 | 29.574 | 30.183 | 27.547 | 21.52 | 22.986 | 28.405 | 38.711 | 36.732 | 52.994 | 31.102 |
| LNR | 27.631 | 26.933 | 25.039 | 28.537 | 23.115 | 20.464 | 19.186 | 24.439 | 28.079 | 29.453 | 24.055 | 15.156 |
| SVR | 47.115 | 58.371 | 56.553 | 66.541 | 59.342 | 56.505 | 40.599 | 52.006 | 67.752 | 75.981 | 69.147 | 37.137 |
| DTR | 24.658 | 26.303 | 25.968 | 31.114 | 26.759 | 21.908 | 23.215 | 27.316 | 30.123 | 32.395 | 17.809 | 14.120 |
| RCV | 27.631 | 26.932 | 25.039 | 28.536 | 23.115 | 20.463 | 19.186 | 24.439 | 28.084 | 29.453 | 24.055 | 15.156 |
| ENet | 28.291 | 27.371 | 25.743 | 28.796 | 23.639 | 20.781 | 19.482 | 24.621 | 28.876 | 30.372 | 24.823 | 15.881 |
| LCV | 27.618 | 26.932 | 25.041 | 28.535 | 23.114 | 20.456 | 19.185 | 24.436 | 28.085 | 29.459 | 24.054 | 15.152 |