Table 6 Polynomial and fourier expansion inspired augmentation results on all the predictive Models.
Final Models | \(\:{\varvec{R}}^{2}\) Score | MSE | RMSE | MAE | EVS | MAPE | SMAPE | Max Error |
|---|---|---|---|---|---|---|---|---|
Decision Tree | 0.99699 | 1931.3746 | 43.94741 | 27.46238 | 0.99699 | 0.00736 | 0.7344 | 178 |
Random Forest + LR | 0.9977 | 1469.6999 | 38.33666 | 23.45429 | 0.99771 | 0.00619 | 0.61692 | 177.99946 |
kNN + LR | 0.99785 | 1372.5477 | 37.04791 | 23.55665 | 0.99786 | 0.00617 | 0.61853 | 122 |
LightGBM | 0.98318 | 10781.187 | 103.83249 | 28.65942 | 0.9832 | 0.00878 | 0.81655 | 1521.0977 |
Gradient Boosting + LR | 0.99472 | 3380.0522 | 58.13821 | 42.25681 | 0.99472 | 0.01171 | 1.16962 | 340.01416 |
AdaBoost + LR | 0.92916 | 45403.068 | 213.07995 | 147.83869 | 0.92933 | 0.04052 | 3.94398 | 1370.6301 |
CatBoost | 0.99896 | 665.76746 | 25.80247 | 14.65029 | 0.99896 | 0.004 | 0.40035 | 128.18601 |
XGBoost | 0.99844 | 998.57142 | 31.60018 | 18.35327 | 0.99845 | 0.00485 | 0.48388 | 171.73657 |
MLP + LR | 0.99860 | 894.58980 | 29.90969 | 20.14475 | 0.99860 | 0.00527 | 0.52620 | 109.22297 |
GRU | 0.99901 | 631.57643 | 25.13118 | 16.58329 | 0.99915 | 0.00443 | 0.44334 | 114.03687 |
LSTM + LR | 0.99884 | 740.51274 | 27.21236 | 17.0143 | 0.99885 | 0.00456 | 0.456 | 177.3122 |
Autoencoders + LR | 0.99853 | 940.02177 | 30.65977 | 18.22995 | 0.99853 | 0.00508 | 0.5083 | 198.1172 |
