From: One-step ahead forecasting of geophysical processes within a purely statistical framework

s/n | Abbreviated name | Category | Model structure information | R algorithm(s) | Implementation notes | |
---|---|---|---|---|---|---|

Hyperparameter optimized using grid search (grid values) | Lagged variable selection procedure (see Table 6) | |||||

12 | NN_1 | Neural networks | Single hidden layer multilayer perceptron | CasesSeries {rminer}, fit {rminer}, lforecast {rminer}, nnet {nnet} | Number of hidden nodes (0, 1, …, 15) | 1 |

13 | NN_2 | 2 | ||||

14 | NN_3 | nnetar {forecast} | 3 | |||

15 | RF_1 | Random forests | Breiman’s random forests algorithm with 500 grown trees | CasesSeries {rminer}, fit {rminer}, lforecast {rminer}, randomForest {randomForest} | Number of variables randomly sampled as candidates at each split (1, …, 5) | 1 |

16 | RF_2 | 2 | ||||

17 | RF_3 | 3 | ||||

18 | SVM_1 | Support vector machines |
Radial basis kernel “Gaussian” function, C = 1, epsilon = 0.1
| CasesSeries {rminer}, fit {rminer}, lforecast {rminer}, ksvm {kernlab} |
Sigma inverse kernel width (2^{n}, n = − 8, − 7, …, 6)
| 1 |

19 | SVM_2 | 2 | ||||

20 | SVM_3 | 3 |