Table 3.

Description and range of values of the hyperparameters for the employed models

Method	Hyperparameters’ description	Abbreviation	Values’ range
ARIMA	Autoregressive order	AR (p)	[1, 6, 7, 8, 9]
	Differentiation order	I (d)	[0, 1]
	Moving average order	MA (q)	[0, 1]
SARIMA	Autoregressive order	AR (p)	[1]
	Differentiation order	I (d)	[0, 1]
	Moving average order	MA (q)	[0, 1]
	Seasonal autoregressive order	P	[0, 1]
	Seasonal difference order	D	[0, 1]
	Seasonal moving average order	Q	[0, 1]
	The number of t-steps for a single seasonal period	s	[7]
MLP_DS	Number of hidden layers	n-hidden-layers	[1, 2, 3]
	Number of neurons per hidden layer	n-nodes	[2, 4, 6, 8, 10]
SVM_DS	Regularization parameter	Cost	[1, 5, 10, 100, 1000]
	Kernel coefficient	Gamma	[1.0, 0.1, 0.01, 0.001, 0.0001]
RF_DS	Number of trees (estimators) in the forest	n-trees	[10, 50, 100, 200, 500]
	Number of features to consider for best splitting	Max-features	[0.6, 0.7, 0.8, 0.9, 1.0]
KNN_DS	Number of neighbors	n-neighbors	[3, 4, 5, 6, 7]
	Weight function	Weights	[“uniform”, “distance”]
MLP	Number of hidden layers	n-hidden-layers	[1, 2, 3]
	Number of neurons per hidden layer	n-nodes	[2, 4, 6, 8, 10]
SVM	Regularization parameter	Cost	[1, 5, 10, 100, 1000]
	Kernel coefficient	gamma	[1.0, 0.1, 0.01, 0.001, 0.0001]
RF	Number of trees (estimators) in the forest	n-trees	[10, 50, 100, 200, 500]
	Number of features to consider for best splitting	Max-features	[0.6, 0.7, 0.8, 0.9, 1.0]
KNN	Number of neighbors	n-neighbors	[3, 4, 5, 6, 7]
	Weight function	Weights	[“uniform”, “distance”]