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 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 comple...

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Bibliographic Details
Published in:Computers & industrial engineering Vol. 62; no. 2; pp. 421 - 430
Main Authors: Azadeh, Ali, Moghaddam, Mohsen, Khakzad, Mehdi, Ebrahimipour, Vahid
Format: Journal Article
Language:English
Published: New York Elsevier Ltd 01.03.2012
Pergamon Press Inc
Subjects:
Algorithms
Analysis of variance
Artificial neural network
Crude oil prices
Economics
Forecasting
Fuzzy
Fuzzy regression
Learning theory
Mathematical models
Mathematical programming
Neural networks
Oil price
Optimization algorithms
Studies
Uncertainty and complexity
Variance analysis
Artificial neural network
Uncertainty and complexity
Fuzzy regression
Forecasting
Oil price
ISSN:0360-8352, 1879-0550
Online Access:Get full text
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Summary:► 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. 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.
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ISSN:0360-8352
1879-0550
DOI:10.1016/j.cie.2011.06.019