Optimal Gasoline Price Predictions: Leveraging the ANFIS Regression Model

Table 9

Summarizes the quantitative assessment of prediction models for gasoline orders utilizing an ensemble approach [40].

Regression models	R-squared		RMSE		Accuracy
	Training sets	Test sets	Training sets	Test sets	Training sets (%)	Test sets (%)
Linear	0.7862	0.7858	0.2614	0.4328	88.67	88.65
AdaBoost	0.8117	0.6531	0.2452	0.5507	90.10	80.82
Extra trees	0.7632	0.6158	0.2753	0.5796	87.36	78.48
Random forest	0.8382	0.5511	0.2274	0.6265	91.55	74.23
XGBoost	0.9823	0.6969	0.0750	0.5148	99.11	83.48