Artificial intelligence methods for oil price forecasting: a review and evaluation

被引：30
作者：
Sehgal N. [1 ]
Pandey K.K. [2 ]
机构：
[1] Jindal Global Business School, O. P. Jindal Global University, Sonipat Narela Road, Near Jagdishpur Village, Sonipat, Sonipat, Haryana, 131001, NCR of Delhi
[2] College of Management and Economic Studies, University of Petroleum and Energy Studies, Energy Acres, P.O. Bidholi, Via-Prem Nagar, Dehradun
来源：
Energy Systems | 2015年 / 6卷 / 4期
关键词：
Feature selection; Hybrid systems; Neural networks; Oil price forecasting; Support vector machine;
D O I：
10.1007/s12667-015-0151-y
中图分类号：
学科分类号：
摘要：
Artificial intelligent methods are being extensively used for oil price forecasting as an alternate approach to conventional techniques. There has been a whole spectrum of artificial intelligent techniques to overcome the difficulties of complexity and irregularity in oil price series. The potential of AI as a design tool for oil price forecasting has been reviewed in this study. The following price forecasting techniques have been covered: (i) artificial neural network, (ii) support vector machine, (iii) wavelet, (iv) genetic algorithm, and (v) hybrid systems. In order to investigate the state of artificial intelligent models for oil price forecasting, thirty five research papers (published during 2001 to 2013) had been reviewed in form of table (for ease of comparison) based on the following parameters: (a) input variables, (b) input variables selection method, (c) data characteristics (d) forecasting accuracy and (e) model architecture. This review reveals procedure of AI methods used in complex oil price related studies. The review further extended above overview into discussions regarding specific shortcomings that are associated with feature selection for designing input vector, and then concluded with future insight on improving the current state-of-the-art technology. © 2015, Springer-Verlag Berlin Heidelberg.
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页码：479 / 506
页数：27
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