Table 3.

Parameter settings of the data forecast

Model	Parameter settings
Linear regression	Default
KNN	N_neighbors = 3
SVR	Kernel = RBF, C = 1e3, epsilon = 0.1
Ridge	Alpha = 1.0, normalize = False
Xgboost	Eta = 0.1, max_depth = 9, gamma = 0.1
Random forest	N_jobs = 1, random_state = 12, n_estimators = 100
AdaBoost	Default
Gradient boosting	Max_depth = 9, min_sample_split = 200
Bagging	Base_estimator = ‘decision tree’
LSTM	Step_size = 4, epochs = 300