Journal
COMPUTERS & INDUSTRIAL ENGINEERING
Volume 62, Issue 2, Pages 421-430Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.cie.2011.06.019
Keywords
Oil price; Uncertainty and complexity; Forecasting; Fuzzy regression; Artificial neural network
Funding
- University of Tehran [27775/1/05]
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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. (C) 2011 Elsevier Ltd. All rights reserved.
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