On the effect of linear data transformations in possibilistic fuzzy linear regression
Fuzzy Sets and Systems
Multiobjective fuzzy linear regression analysis for fuzzy input-output data
Fuzzy Sets and Systems
Fuzzy linear regression with fuzzy intervals
Fuzzy Sets and Systems
Multi-objective fuzzy regression: a general framework
Computers and Operations Research - Special issue on artificial intelligence and decision support with multiple criteria
A note on fuzzy regression model with fuzzy input and output data for manpower forecasting
Fuzzy Sets and Systems - Theme: Learning and modeling
Forecasting of the electric energy demand trend and monthly fluctuation with neural networks
Computers and Industrial Engineering
Oil Price Forecasting with an EMD-Based Multiscale Neural Network Learning Paradigm
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007
Forecasting the Price Development of Crude Oil with Artificial Neural Networks
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living
Crude Oil Price Forecasting with an Improved Model Based on Wavelet Transform and RBF Neural Network
IFITA '09 Proceedings of the 2009 International Forum on Information Technology and Applications - Volume 01
FCC '09 Proceedings of the 2009 ETP International Conference on Future Computer and Communication
Fuel Oil Price Forecasting Using Symbiotic Evolutionary Immune Clustering Neural Network
ICICTA '09 Proceedings of the 2009 Second International Conference on Intelligent Computation Technology and Automation - Volume 01
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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.