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Official Journal of the Asia Oceania Geosciences Society (AOGS)

Geoscience Letters Cover Image

Table 5 Univariate time series forecasting methods of this study (part 2): machine learning methods

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 (2n, n = − 8, − 7, …, 6) 1
19 SVM_2 2
20 SVM_3 3