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


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.

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Fig. 1


\(R^2\) :

Coefficient of determination




Analog complexity


Auto-correlation function


Autoregressive conditional interval model with exogenous explanatory interval variable


Absolute error


Artificial intelligent


Adaptive linear neural network


AI framework of Amin-Naseri et al.


Artificial Neural Network


Asymmetric power ARCH


Annualised return


Autoregressive integrated moving average


Broyden–Fletcher–Goldfarb–Shanno–Quasi Newton

BiP Sig:

Bipolar sigmoid


Bias learning rule


Boltzmann Neural Network




Back-Propagation Neural Network


Bayesian regulation


Brent crude oil market


Bayesian vector auto-regression


Correlation analysis


Conditionally autoregressive VaR


Cluster classifier


Crisis index




Day ahead




Direct strategy


Decomposition based Neural Networks


Directional statistics


Delta test


Dubai oil market


Error correction model


Exponential GARCH


Expectation maximization


Empirical mode decomposition


Elman Neural Network


Forward backward selection


Fractionally integrated GARCH


Full information maximum likelihood


Functional Link Neural Network


Fuzzy model


Fuzzy Neural Network


NYMEX future prices


Genetic Algorithm


Generalized autoregressive conditional heteroskedasticity


Geometric Brownian process


Gradient descent


Gradient descent BEP


Generalized Pattern Matching Genetic Algorithm


General Regression Neural Network


Grey system model


Gamma test


Harr a Trous


Hidden Markov Model


Hannan–Quinn info criterion


Hit rate


Hyperbolic tangent sigmoid


Hull white with binomial tree


Instance based learning


Integrated GARCH


Inverse Gaussian process


Judgemental criterion


Genetic Programming framework of Kaboudan


Linear relative inventory model






Levenberg–Marquardt Algorithm


Logarithmic sigmoid


Least Square Error




Month ahead


Mean Absolute Error


Mean absolute percentage error


Manual feature extraction


Multi-layered Feed Forward Neural Network


Mixture of Gaussian NN


Mean reverting process


Mean Squared Error


Non-linear relative inventory model


Normalised Mean Squared Error


Neural networks




Naïve random walk


Noise-to-signal ratio


Ordinary Least Square


Ornstein–Uhlenbeck Model


Partial autocorrelation function


Power ARCH


Percentage of correct predictions


Persian Gulf region prices


Partial mutual information


Prediction rate


Pattern modelling in recognition system approach


Radial basis function


Recursive strategy


Regression model


Relative change of moving average


Regime Markov switching stochastic volatility model


Root Mean Squared Error


Recurrent Neural Network


Regime switching


Return transformation


Random walk


Standard SVM


Step ahead




Stochastic model


Symmetric MAPE


Smoothing procedure


Signal-to-noise ratio


Self-organizing MLP


Spot prices


Scaling range


Sum of Square Error


EIA’s short-term energy outlook econometric model


Support vector machine


Support vector regression


Trial and error method


Threshold GARCH


Text mining


Time period ahead


Tangent sigmoid




Value-at-risk model


Vector error correction model




Week ahead


AI framework of Wang et al.


Without crisis index


Wavelet decomposition ensemble


Wavelet Neural Network


Without smoothing procedure


Wavelet transform


West Texas Intermediate Crude Oil Market


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Sehgal, N., Pandey, K.K. Artificial intelligence methods for oil price forecasting: a review and evaluation. Energy Syst 6, 479–506 (2015).

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  • Neural networks
  • Feature selection
  • Support vector machine
  • Hybrid systems
  • Oil price forecasting