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

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Abstract

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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Abbreviations

\(R^2\) :

Coefficient of determination

A:

Annual

AC:

Analog complexity

ACF:

Auto-correlation function

ACIX:

Autoregressive conditional interval model with exogenous explanatory interval variable

AE:

Absolute error

AI:

Artificial intelligent

ALNN:

Adaptive linear neural network

AMIN:

AI framework of Amin-Naseri et al.

ANN:

Artificial Neural Network

APARCH:

Asymmetric power ARCH

AR:

Annualised return

ARIMA:

Autoregressive integrated moving average

BFGS:

Broyden–Fletcher–Goldfarb–Shanno–Quasi Newton

BiP Sig:

Bipolar sigmoid

BLR:

Bias learning rule

BNN:

Boltzmann Neural Network

BP:

Back-propagation

BPNN:

Back-Propagation Neural Network

BR:

Bayesian regulation

Br:

Brent crude oil market

BVaR:

Bayesian vector auto-regression

CA:

Correlation analysis

Ca-Var:

Conditionally autoregressive VaR

CC:

Cluster classifier

CrI:

Crisis index

D:

Daily

DA:

Day ahead

Db:

Daubechies

DirS:

Direct strategy

DNN:

Decomposition based Neural Networks

DS:

Directional statistics

DT:

Delta test

Du:

Dubai oil market

ECM:

Error correction model

EGARCH:

Exponential GARCH

EM:

Expectation maximization

EMD:

Empirical mode decomposition

ENN:

Elman Neural Network

FBS:

Forward backward selection

FIGARCH:

Fractionally integrated GARCH

FIML:

Full information maximum likelihood

FLNN:

Functional Link Neural Network

FM:

Fuzzy model

FNN:

Fuzzy Neural Network

FP:

NYMEX future prices

GA:

Genetic Algorithm

GARCH:

Generalized autoregressive conditional heteroskedasticity

GB:

Geometric Brownian process

GD:

Gradient descent

GDX:

Gradient descent BEP

GPMGA:

Generalized Pattern Matching Genetic Algorithm

GRNN:

General Regression Neural Network

GSM:

Grey system model

GT:

Gamma test

HaT:

Harr a Trous

HM:

Hidden Markov Model

HQIC:

Hannan–Quinn info criterion

HR:

Hit rate

HTS:

Hyperbolic tangent sigmoid

HWBT:

Hull white with binomial tree

IBL:

Instance based learning

IGARCH:

Integrated GARCH

IGP:

Inverse Gaussian process

JC:

Judgemental criterion

KAB:

Genetic Programming framework of Kaboudan

L-RIM:

Linear relative inventory model

LD:

Log-differenced

Lgs:

Logistic

LM:

Levenberg–Marquardt Algorithm

LS:

Logarithmic sigmoid

LSE:

Least Square Error

M:

Monthly

MA:

Month ahead

MAE:

Mean Absolute Error

MAPE:

Mean absolute percentage error

MFA:

Manual feature extraction

MLP:

Multi-layered Feed Forward Neural Network

MoGNN:

Mixture of Gaussian NN

MRP:

Mean reverting process

MSE:

Mean Squared Error

NL-RIM:

Non-linear relative inventory model

NMSE:

Normalised Mean Squared Error

NN:

Neural networks

NORM:

Normalization

NRW:

Naïve random walk

NSR:

Noise-to-signal ratio

OLS:

Ordinary Least Square

OU:

Ornstein–Uhlenbeck Model

PACF:

Partial autocorrelation function

PARCH:

Power ARCH

PCP:

Percentage of correct predictions

PGRP:

Persian Gulf region prices

PMI:

Partial mutual information

PR:

Prediction rate

PRMS:

Pattern modelling in recognition system approach

RBF:

Radial basis function

RecS:

Recursive strategy

RM:

Regression model

RMA:

Relative change of moving average

RMS:

Regime Markov switching stochastic volatility model

RMSE:

Root Mean Squared Error

RNN:

Recurrent Neural Network

RS:

Regime switching

RT:

Return transformation

RW:

Random walk

S-SVM:

Standard SVM

SA:

Step ahead

Sig:

Sigmoid

SM:

Stochastic model

SMAPE:

Symmetric MAPE

SMP:

Smoothing procedure

SNR:

Signal-to-noise ratio

SoMLP:

Self-organizing MLP

SP:

Spot prices

SR:

Scaling range

SSE:

Sum of Square Error

STEO:

EIA’s short-term energy outlook econometric model

SVM:

Support vector machine

SVR:

Support vector regression

TE:

Trial and error method

TGARCH:

Threshold GARCH

TM:

Text mining

TPA:

Time period ahead

TSig:

Tangent sigmoid

TSK:

Takagi–Sugano–Kang

VaR:

Value-at-risk model

VECM:

Vector error correction model

W:

Weekly

WA:

Week ahead

WANG:

AI framework of Wang et al.

WCI:

Without crisis index

WDE:

Wavelet decomposition ensemble

WNN:

Wavelet Neural Network

WSP:

Without smoothing procedure

WT:

Wavelet transform

WTI:

West Texas Intermediate Crude Oil Market

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Authors and Affiliations

  1. Jindal Global Business School, O. P. Jindal Global University, Sonipat Narela Road, Near Jagdishpur Village, Sonipat, Sonipat, Haryana, 131001, NCR of Delhi, India

    Neha Sehgal

  2. College of Management and Economic Studies, University of Petroleum and Energy Studies, Energy Acres, P.O. Bidholi, Via-Prem Nagar, Dehradun, 248007, India

    Krishan K. Pandey

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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). https://doi.org/10.1007/s12667-015-0151-y

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