A flexible neural network-fuzzy mathematical programming algorithm for improvement of oil price estimation and forecasting

This paper presents a flexible algorithm based on artificial neural network (ANN) and fuzzy regression (FR) to cope with optimum long-term oil price forecasting in noisy, uncertain, and complex environments. The oil supply, crude oil distillation capacity, oil consumption of non-OECD, USA refinery capacity, and surplus capacity are incorporated as the economic indicators. Analysis of variance (ANOVA) and Duncan’s multiple range test (DMRT) are then applied to test the significance of the forecasts obtained from ANN and FR models. It is concluded that the selected ANN models considerably outperform the FR models in terms of mean absolute percentage error (MAPE). Moreover, Spearman correlation test is applied for verification and validation of the results. The proposed flexible ANN–FR algorithm may be easily modified to be applied to other complex, non-linear and uncertain datasets.

► This paper presents a flexible algorithm based on artificial neural network and fuzzy mathematical programming.
► It is capable of coping with optimum long-term oil price forecasting in noisy, uncertain, and complex environments.
► The algorithm may be easily modified to be applied to other complex, non-linear and uncertain datasets.