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Energy Syst
DOI 10.1007/s12667-015-0151-y
ORIGINAL PAPER
Artificial intelligence methods for oil price forecasting:
a review and evaluation
Neha Sehgal1·Krishan K. Pandey2
Received: 19 September 2014 / Accepted: 8 May 2015
© Springer-Verlag Berlin Heidelberg 2015
Abstract Artificial intelligent methods are being extensively used for oil price fore-
casting 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 tech-
niques 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 vari-
ables 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 regard-
ing 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.
Keywords Neural networks ·Feature selection ·Support vector machine ·
Hybrid systems ·Oil price forecasting
BKrishan K. Pandey
krishan.pandey@gmail.com
Neha Sehgal
nmehra@jgu.edu.in
1Jindal Global Business School, O. P. Jindal Global University, Sonipat Narela Road,
Near Jagdishpur Village, Sonipat, Sonipat, Haryana 131001, NCR of Delhi, India
2College of Management and Economic Studies, University of Petroleum and Energy Studies,
Energy Acres, P.O. Bidholi, Via-Prem Nagar, Dehradun 248007, India
123
N. Sehgal, K. K. Pandey
List of symbols
R2Coefficient of determination
A Annual
AC Analog complexity
ACF Auto-correlation function
ACIX Autoregressive conditional interval model with exogenous explana-
tory 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
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Artificial intelligence methods for oil price forecasting...
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
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N. Sehgal, K. K. Pandey
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
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Artificial intelligence methods for oil price forecasting...
WT Wavelet transform
WTI West Texas Intermediate Crude Oil Market
1 Introduction
Fossil fuels currently account for 87 % of primary energy demand and projected to
still make up 82 % of the global total by 2035. According to BP [1], oil remains the
world’s primary fuel, accounting to 33.1 % of global energy consumptions. Oil will
remain the energy type with the largest share for most of the projected periods and
will continue to play a foremost part in satisfying world energy needs [2]. Oil price
act as a key component dominating investment picture for years to come on. They act
as a key variable in evaluation of economic development, energy policy decisions and
stock markets [1]. A prior knowledge of oil prices fluctuations helps oil producers to
make decisions about the increase or decrease in production levels accordingly.
Oil prices helps strategically in macroeconomic projections and macroeconomic
risk analysis for central and private banks. They are helpful in predicting recession
in business cycles [3]. They are helpful in planning regulatory policies regarding
taxes & standards. Businesses dependent on oil will be benefited as they will be in
position to take measures to control manufacturing and sales of their products in
line with expected trend of forecast oil prices. Accurate forecasting helps Non-OPEC
countries to take effective measures so their growth remains robust and thus benefited
consumers. Further, economic policies can be formulated in way to overcome recession
and unemployment.
2 Econometric models
Forecasting of crude oil prices is an important task for better investment manage-
ment, macroeconomic policies and risk management. It is important to analyses the
probabilistic assumption of oil prices in terms of normality, linearity and serial cor-
relation [4]. To forecast crude oil prices, a variety of approaches have been proposed
by numerous authors employing time series [5–10], financial models [11,12] and
structural models [13–18].
2.1 Time series model
Time series analysis is a method of forecasting that focuses on the historical behav-
iour of dependant variable. Oil prices are assumed to be normally distributed in many
studies but their departure from normal distribution was disregardeddue to misinterpre-
tation of Central Limit Theorem [4,19]. Crude oil prices are found to be non Gaussian.
Forecasting crude oil prices through fundamental method is a complex task due to
uncertainty, noisiness and non-stationary inbuilt in indicators that drive them. There-
fore, time series models provide an alternative to analyse and predict future movements
based on past behaviour of oil prices [20]. The price-forecasting models based on time-
series approach have been further classified into three subsets as shown in Fig. 1.
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N. Sehgal, K. K. Pandey
Fig. 1 Classification of time series oil price-forecasting models
The summary of time series forecasting models based on type of model being
employed and methods used by researchers have been presented in Table 1. Stochastic
models are inspired by financial literature and are widely applied in forecasting of oil
prices. There are several stochastic models which have been employed for modelling
and forecasting of crude oil prices such as Random Walk [6,7,9,21,22], Mean Revert-
ing Processes [23], Brownian Motion Processes [24], Ornstein–Uhlenbeck Processes
[24], Inverse Gaussian Process [19] and Jump Diffusion Processes [25].
Regression type models are based on the relationship between oil price and
number of exogenous variables that are known or can be estimated. The most com-
mon approaches employing regression models are ARIMA models [12,26,27] and
GARCH-family models [6,8,26,28–30]. Arouri et al. [5] employs GARCH models
to forecast conditional volatility of spot and future oil prices with structural breaks
for better forecasting performance. Huang et al. [31] and Hou [28] presented superior
performance of non-parametric GARCH models relative to parametric GARCH mod-
els (in-sample and out-of-sample volatility forecasts). Researchers concluded that
non-linear dynamical approach is more appropriate for characterizing and predict-
ing crude oil prices than linear approach [32,33]. The parameters of forecasting
models for crude oil prices have been estimated by either Least Square Method
[12,23,34–38], Full Information Likelihood Method [16], Kalman Filter [23,24]or
under Bayesian Framework [39]. However, these numerous estimation algorithms have
failed to achieve high prediction accuracy. Stochastic models involving certain char-
acteristics of oil prices and regression models have been kept outside the scope of this
review.
A review of these econometric time series models for oil price forecasting has been
presented by Frey et al. [40].
Table 1provide summary of time series models for crude oil price forecasting.
In recent times, artificial intelligent models are extensively being used to capture
unknown or too complex structure in the time series. Researchers have used artifi-
cial intelligent model based approach for oil price forecasting in more than 50 % of
the studies listed in Table 1. Out of thirty six studies listed in Table 1, twenty eight
studies have considered WTI crude oil spot prices as dependent variable in their stud-
ies. Section 3covers various artificial intelligent models ranging from single models
(e.g. neural networks, support vector regression, wavelets) to more complex hybrid
versions.
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Artificial intelligence methods for oil price forecasting...
Tab le 1 Summary of time series models for crude oil price forecasting
References Oil market Model type Methods
[9] WTI SM, RM RW, VaR, ECM
[19]WTI SM IGP
[81]WTI RM ARIMA
[23] WTI RM, SM OLS , MRP
[77]Br AI WNN
[24]FP SM GB,OU
[84] WTI, Br RM GARCH
[26] FP RM, AI ARIMA, GARCH, ANN
[85] FP AI GSM
[10] WTI AI FM
[59] WTI, Br AI SVM
[67] WTI AI SVM
[80] WTI, Br AI ANN
[74] WTI AI WNN
[7] WTI,Br AI,RM,SM WDE,RW,ARMA
[76] WTI AI ANN, WDE
[5] WTI, FP RM GARCH
[86] FP RM RS-EGARCH
[73] WTI, Br AI GA + ANN
[79] WTI AI FNN
[87]WTI AI RMS
[28] WTI RM GARCH
[88] FP RM GARCH
[89] WTI AI WDE
[30] WTI, Br RM GARCH
[56] WTI RM RS-GARCH
[22] WTI SM, RM VECM, NRW, ARIMA
[90] WTI RM CA-VaR
[69] FP AI GA + ANN
[61] WTI AI SVR
[63] WTI AI SVR
[75] WTI, Br, Du AI WNN
[78] WTI, Br AI FNN
[29] WTI, Br, Du RM CGARCH, FIGARCH, IGARCH
[6] WTI SM, RM RW, HM, ARIMA, GARCH,
EGARCH, TGARCH, PARCH,
CGARCH
[8] SP RM GARCH, EGARCH,APARCH,
FIGARCH
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N. Sehgal, K. K. Pandey
2.2 Fundamental models
Fundamental models predict oil prices based on their relationship with economic,
financial, social and political indicators that drive them. This study assumes financial
and structural models as part of fundamental models. Crude oil prices have been
influenced by large number of factors which are complex, noisy, and uncertain [20].
There is no single indicator which can provide a comprehensive portrait of how prices
can be determined. Each indicator can give us a snapshot of present condition and
modelling of these significant snapshots together provides a clear picture of direction
of oil prices. Similar to time series models, fundamental models can also be classified
into two major classes: Regression models and artificial intelligent based models
as shown in Table 2. This table enlists the analytical methods used by authors for
forecasting crude oil prices.
Globalization hypothesis holds that oil prices (WTI-Brent, WTI-Dubai, WTI-Maya
and Dubai-Maya) move together and exhibit greater conditional dependency [41];
therefore, most of the study listed in Table 2considers WTI spot crude oil prices as
benchmark price. It is evident from Table 2that around 50 % of the studies have incor-
porated artificial intelligent models for forecasting oil prices. As evident from Table 2,
researchers have preferred ordinary least square methods for parameter estimation in
a regression model. The different input variables, along with the class they belong to,
used by different researchers are presented in the next section.
2.2.1 Factors driving oil prices
Oil prices had shown upward trend in 1996 but prices declined drastically by end
of 1998. As a consequence of cuts in OPEC production targets, oil prices increased
again in late 2000. The impact of such extreme events is of prime importance as they
effect the direction of oil prices and thereby the objective of increasing the predication
accuracy of crude oil prices. It is important to identify the key indicators driving crude
oil prices (during the time-frame of happening of such extreme events) for designing
better structural forecasting models. In 2001, 9/11 attack led to increase in volatility of
oil prices that soared oil prices till 2004. Oil prices have been steadily rising for several
years and in July 2008 stood at a record high of $145/bbl due to low spare capacity.
Later, it declined due to global economic crisis at the end of 2008 and then recovered
to around $75/bbl by 2010. In 2013, oil prices have set record by surpassing $100/bbl
for the first time in that year (in money-of-the-day terms). According to EIA [42], oil
prices were rising sharply because global demand (especially in China) was surging
and production wasn’t adequate to keep up. This led to a boom in unconventional
oil production by major energy companies across globe and world’s oil supply kept
growing by mid-2014. On the other side, due to slowdown in Asia and Europe, the
oil demand began weakening. This combination of steadily rising supply and weaker-
than-expected demand together with OPEC in favour of letting prices continue to fall
pushed oil prices to drop around $55/bbl by end of 2014. This rise or decline in oil prices
stimulates for studying in detail the factors behind movements in oil prices. There are
large number of factors, which are complex, noisy, and uncertain influencing crude oil
prices [20]. Understanding complex oil price movements and indicators driving them
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Artificial intelligence methods for oil price forecasting...
Tab le 2 Summary of
fundamental models for crude
oil price forecasting
References Oil market Model type Methods
[57] WTI AI ANN
[43] WTI AI WNN
[35]WTI RM OLS
[36]WTI RM OLS
[37]WTI RM OLS
[38] WTI RM OLS, ECM
[46]WTI RM OLS
[17] WTI AI WNN
[34]WTI RM OLS
[16] WTI RM OLS, FIML, ECM
[12] WTI RM OLS, ARMA
[66]Br AI GRNN
[18] WTI AI ANN, TM
[64] WTI AI FNN
[14] PGRP AI ANN
[50] WTI RM VECM
[51]WTI RM ACIX
[55] WTI AI WDE
[52]FP AI WDE
[53] FP RM GARCH
[91] FP RM GARCH
[33] Br, WTI, Du RM ESTAR
[92] WTI RM CGARCH
[39] WTI RM BVaR
[93]FP RM ECM
[65] SP AI ANN
[60] WTI AI SVR
[62] NF AI SVR
[68]SP AI GA
[74]WTI;BrAI WNN
[72] WTI AI WNN
was the impetus for Energy Information Administration (EIA) to launch a monthly
report to assess the financial, trading and physical market factors that influence oil
prices.
Beside geopolitical and economic events, any fluctuation in demand or supply side
also creates imbalance in the market that critically impact oil market participants and
makes markets unpredictable. The demand-supply framework plays a crucial role to
an extent that it determines the directions of crude oil prices but has not been sole
indicator that drives oil prices. Researchers have considered enormous factors such
as GDP, inventories, emerging economies and stock market fluctuations to study their
influence on oil prices (Table 3covers this in detail). There is no solitary indicator
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N. Sehgal, K. K. Pandey
Tab le 3 Summary of factors
influencing oil prices Class Input variable Key
Supply OPEC production V1
Non-OPEC production V2
World production V3
Oil supply V4
Demand OECD consumption V5
China consumption V6
India consumption V7
Seasonal demand V8
Global demand V9
Non-OECD consumption V10
Inventories U.S. refinery capacity V11
OPEC total liquid capacity V12
U.S. gasoline ending stocks V13
OECD stocks V14
U.S. ending stocks V15
U.S. petroleum imports from OPEC V16
U.S. petroleum imports from non-OPEC V17
U.S. crude oil imports from OPEC V18
U.S. crude oil import from non-OPEC V19
OECD industrial inventory level V20
Crude oil distillation capacity V21
Price Historical prices V22
Heating oil spot price V23
Gasoline oil spot price V24
Natural gas spot price V25
Propane spot price V26
NYMEX crude oil futures V27
NYMEX heating oil futures V28
Reserves OPEC reserves V29
OECD reserves V30
No. of well drilled V31
Economy GDP growth rate V32
U.S. dollar nominal effective exchange rate V33
Foreign exchange of GBP/USD V34
Foreign exchange of YEN/USD V35
Foreign exchange of Euro/USD V36
U.S. inflation rate V37
U.S. consumer price index V38
Population of developed countries V39
Population of less developed countries V40
S&P500 V41
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Artificial intelligence methods for oil price forecasting...
Tab le 3 continued Class Input variable Key
Gold prices V42
Producer price index V43
World events World event impact factor V44
OPEC quota tighten (April 99) V45
Sept 11, 2001 attack V46
Properties API density V47
Sulphur content V48
Country V49
Time V50
No. of weeks V51
(lags, future prices or macroeconomic variables) that can provide a complete picture
on how prices have been determined, however there are few key indicators that have
governed and ruled crude oil prices. The key indicators can give us snapshots of
fluctuations in oil prices and modelling of these significant snapshots only (as input
variables) can give a clear picture on directions of oil prices. The fluctuations in these
factors cause complex, volatile, non-linear and chaotic tendency of crude oil prices,
therefore, it is important to find the key strategic indicators that are ruling crude oil
prices from decades. Thus, it has become crucial to develop predictive models using
various influential factors that drive crude oil prices to understand the complex and
dynamic nature of oil prices.
This review has identified petroleum inventory level as a virtuous market indicator of
change in crude oil price. Inventory levels have been a measure of balance or imbalance
between production and demand [43]. Ye et al. proposed a linear forecasting model
using relative inventory level as input for oil prices [36] but later improved Linear-
RIL model to non-linear-RIL model due to dynamic relationship between them [37].
Pang et al. [43] proposed to consider both crude oil inventory level and petroleum
product inventory level as input factors for better forecasting performance. Weiqi
et al. [15] constructed a structural econometric model using relative inventory and
OPEC production as explanatory variables for short-run oil price forecasts. Other
than inventories level, variables like production, net imports and forward prices were
taken as independent variables to estimate spot prices by Considine and Heo [44]. The
evidence for the non-linear relationship between GDP growth and oil prices has been
examined by Hamilton [34] and Kim [45].
Zamani [38] examined a short term quarterly econometric forecasting model using
OECD stocks, Non-OECD demand and OPEC supply to forecast WTI crude oil prices.
Ye et al. [35] considered the possible substitution for large proportion of world demand
and inventory (in form of OECD demand) as input variable compared to U.S demand
alone. Dées et al. [46] assessed a structural econometric model to show the immediate
impact of OPEC quota decisions and capacity utilization on oil prices. According
to BP [1], emerging economies (especially Asia) accounted for major net growth
in energy consumption whereas OECD demand remains falling for a third time in
last 4 years. Ratti and Vespignani [47] has highlighted that intense increase in China
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N. Sehgal, K. K. Pandey
and India liquidity led to increase in real oil price and production. Li and Lin [48]
has provided evidence indicating demand from China and India as leading driver in
the world oil pricing system since 2003. Zhang and Wang [49] indicated a greater
contribution (95.71 %) of crude oil future markets in price discovery function of
spot price. Alvarez-Ramirez et al. [13] used fourier analysis to examine the strong
relationship between U.S macro economy and crude oil prices. Dées et al. [16] has
examined the dynamic relationship between oil prices and OPEC capacity utilization.
Murat and Tokat [50] examined the forecasting power of crack spread futures under
vector error correction framework to predict spot oil markets. Yang et al. [51] studied
the influence of European debt crisis and financial crisis on crude oil prices with the
proposed Autoregressive Conditional Interval Model with Exogenous Explanatory
Interval Variable (ACIX). He et al. [52] used error correction models to examine the
influence of Kilian economic index (as global activity indicator) on crude oil prices.
Bu [53] examined the relationship between speculative traders’ position and crude
oil future prices using estimates of GARCH model. Basher and Haug [54] proposed a
structural vector autocorrelation model to investigate the dynamic relationship between
emerging markets, stock prices and oil prices. Chai et al. [39] developed a oil price VaR
model based on path-analysis using core influential factors. Zhang et al. [55] proposed
an empirical mode decomposition-based event analysis method to estimate the impact
of extreme events on crude oil prices. Fong and See [56] suggested regime switching
models framework to study factors driving crude oil prices volatility. Ignoring the clear
evidence of presence of non-linearity or structural breaks in the oil price series can lead
to false imprint on predictability and persistence. Oil prices are shown more sensitive
to any change in oil supply by Chai et al. [39] but weekly dependent on exchange rates
by Reboredo [41]. The study of factors driving oil prices that are considered in various
stochastic or regression models of oil prices forecast is a major area of research and
has been kept outside the scope of this review.
Despite above mentioned attempts, oil price prediction has remained a difficult
problem due to its complex non-linear and time-varying nature. In addition, recent stud-
ies lay emphasis on developing structural econometric models for forecasting crude
oil prices without focusing on finding the key drivers of oil prices. Most recently, a cat-
egory of artificial intelligent models have emerged and are being attempted to predict
oil prices. AI based framework for oil price prediction are discussed in detail in Sect. 3.
The factors influencing oil prices may be classified within the categories: C1—supply,
C2—demand, C3—inventories, C4—price, C5—reserves, C6—economy, C7—world
events, and C8—properties.
There are as many as fifty one variables used in different AI related studies for
crude oil price forecasting. A detailed list of factors driving oil prices considered in
artificial intelligent models, along with the class to which they belong are presented
in Table 3. In order to build an effective model, careful attention should be paid
on selecting informative and influential inputs which cause changes in prices [57].
However, until recently, the input variables of oil price forecast have been selected
on judgemental criteria or trial and error procedures [58–63]. This review discovers
that most of the studies were concentrated on non-linearity, non-stationary and time
varying properties of oil prices but seldom focused on feature selection method for
selecting significant inputs to improve forecasting accuracy. The study identifies that
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Artificial intelligence methods for oil price forecasting...
historical oil prices (either daily, weekly or yearly) are the most popular input variables
used by researchers. Abdullah [18] has used 22 input variables from the categories
as mentioned in Table 3to achieve high prediction accuracy but the variables were
selected on judgemental criteria. To handle optimal long-term oil price forecasting,
Azadeh et al. [64] developed a flexible algorithm based on artificial neural networks
and fuzzy regression by using oil supply, crude oil distillation capacity, oil consumption
of Non-OECD, USA refinery capacity and surplus capacity as economic indicators.
The study concluded ANN models outperform FR models in terms of mean absolute
percentage error (MAPE). Section 3discusses in detail the characteristics of different
types of artificial intelligent models that used factors listed in Table 3as input variables
for oil price forecasting.
3 Review of methodology
In this section, the studies are segregated on the basis of type of models, starting
from single models (such as neural networks, support vector regression, wavelets,
genetically evolved models) to more complex and hybrid models. Recently, hybrid
models are been extensively used for building oil price forecasting models as they
overcome the limitations of single models and provide better forecasting accuracy.
3.1 Neural network based models
In the past , neural networks were being used extensively for oil price forecasting.
Neural Networks can model richer dynamics and can approximate any continuous
function of inputs [57]. In this category, seven researchers have forecasted oil prices
using artificial neural networks as single model. There are many research studies that
suggest integration of neural networks with other traditional methods (such as support
vector regression, genetic algorithm, or wavelets) by mean of hybrid approach for
improving the prediction performance. These studies are discussed in detail in Sect.
3.5. In Table 4, information regarding the data, time scale, input variables for the
study, method of input variable selection and preprocessing techniques employed
are discussed. Researcher have utilized neural networks for all major oil markets. It
is evident from the Table 4that neural networks can handle large number of input
variables.
Abdullah and Zeng [18] integrated 22 quantitative input variables (sub-factors of
demand, supply, economy, inventory and population) together with the qualitative data
(collected from experts’ view and news) using neural networks to predict oil prices for
long and short term time period. The authors have utilized manual feature extraction
method for finding significant input variables for the study. Most of the studies have
pre-processed the raw price data either by scaling range, normalization or cluster
classification as shown in Table 4.
This review identified that most of studies have selected input variables based on
judgemental criteria or trail and error basis. In Table 5, forecasting performance of
various neural networks models has been compared.
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N. Sehgal, K. K. Pandey
Tab le 4 Data characteristics, preprocessing technique, input variables and its selection method
References Oil
market
Time
scale
Input variable Variable
selection
Preprocessing
technique
[57] WTI D V22, V27 JC RMA, NORM
[26]NFDV22 JC–
[66] Br M V11, V32, V33, V12, V1, V13 TE CrI is formed
[14] PGRP M V47, V48, V49, V50 SA SR
[58]SP–V22 JCSR
[18] WTI M V1, V2, V5, V6, V7, V14, V15,
V16, V17, V18, V19, V29,
V30, V31, V34, V35, V36,
V32, V37, V38, V39, V40
MFA LD
[65] SP D V22, V23, V24, V25, V26 CA CC
Tab le 5 Forecasting performance comparison of neural network models
References Training
data (%)
Testing
data (%)
Forecast
horizon
Comparison with
other models
Level of accuracy
[57] 90 10 3 DA – RMSE: 0.53–0.78 ;
HR: 53–79
[26] 86 14 1 TPA ARIMA, GARCH MSE: 8.14; RMSE:
2.85; MAE: 2.04
[66]86141MAWCI-GRNN MSE:−1.48–9.84
[14] 70 30 – – MSE: 7.24–8.82
[58]––5DA– NMSE:−0.35; DS:
61; SNR: 25.37;
AR: 92
[18]8020– TEI@I,
EMD-FNN-ALNN
RMSE: 2.26;; NMSE:
0.009; DS: 94
[65]80201MARM MSE:−2.15–4.73
Neural networks showed superior results compared to benchmark TEI@I method-
ology and ARIMA-GARCH models as shown in Table 5. The models are validated
using Root Mean Square Error (RMSE), Hit Rate (HR), Mean Square Error (MSE),
Mean Absolute Error (MAE), Normalized Mean Square Error (NMSE), Annualized
Return (AR) and Directional Statistics (DS). Malliaris and Malliaris [65] studied five
inter-related energy products for forecasting one-month ahead prices using neural net-
works. The results thus obtained through neural network consistently led to a MSE
less than half than that of the regression predictions. Malliaris and Malliaris [65]used
correlation analysis to find significant input variables for their study. The model archi-
tecture of seven studies considered under this category is shown in Table 6. Mahdi
et al. [58] examined three different neural network models: Multi-layered Perceptron
(MLP), Functional Link Neural Network (FLNN) and Self-Organized MLP (SoMLP)
for the oil price series. Mahdi et al. [58] compared the prediction capability of SoMLP
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Artificial intelligence methods for oil price forecasting...
Tab le 6 Neural Networks model’s architecture
References NN type Learning
algorithm
Hidden
neurons
Activation
function
[57] MLP, RNN LM 1–10 Sig
[26] MLP GD 5 HTS, Id
[66]GRNN – TE–
[14] MLP BP 15 LSig
[58] MLP, FLNN, SoMLP BP, IA – –
[18] MLP BP TE Sig
[65]– – 20–
with MLP and FLNN for ten different data sets including oil prices. The experimental
results thus demonstrated that all neural networks performed better by using stationary
data and failed to generate profits while using non-stationary data.
Haider et al. [57] presented a short term forecasting model to understand oil price
dynamics based on multi-layer feed forward neural network. Several transformation
methods were tested with original data and results showed that relative change of sim-
ple moving average is the best method amongst other methods. Moshiri and Foroutan
[26] examined chaos in daily crude oil future prices using BDS and Lyapunov test.
The results indicated that future prices follow complex non-linear dynamic process
and showed superiority of ANN model as compared to ARIMA and GARCH models.
Movagharnejad et al. [14] designed a neural network to predict the prices of seven
different crude oils in Persian Gulf region, provided that the benchmark light oil of
Saudi Arabia is known or predicted by another hybrid forecasting method.
Further, this review identifies that number of hidden neuron varies across studies.
Haider et al. [57] has fixed the number of neurons in hidden layer ranging from 1–10
while few authors [14,26,65] have fixed a constant value based on judgemental criteria
for number of neurons in hidden layer. There is no rule of thumb applied by researchers
for finding the optimal number of neuron to be set for hidden layer. Alizadeh and
Mafinezhad [66] proposed a general regression neural network forecasting model for
Brent crude oil price with particular attention on finding number of features as input
data to achieve best performance.
Most of the studies have used sigmoid function as preferable activation function
as shown in Table 6. It is evident from the Table 6that the multi-layered perceptron
neural network with back propagation as the learning algorithm is the most popular
among researchers for price forecasting.
3.2 Support vector regression models
Oil prices are complex series with mixture of linear and non-linear characteristics
underlying data generating processes of different nature. He et al. [63] introduced
morphological component analysis to explore the complex nature underlying oil prices.
There are few authors who have tested for non-linearity [61,63,67] and normality
123
N. Sehgal, K. K. Pandey
Tab le 7 Data characteristics, preprocessing technique, input variables and its selection method
References Oil
market
Time
scale
Input
variable
Input
selection
Preprocessing
[59]WTI;BrWV22 JCSR
[60] WTI W V22, V27, V8, V51, V44, V9 JC SR
[61]WTIDV22 JCRT
[62] NF D V22, V33, V28, V41 JC CC
[63] WTI D V22 JC WT; RT
[67]WTIMV22 JCRT
assumptions [61,63] of oil price series. Support vector regression has an advantage
of reducing the problem of over-fitting or local minima. Khashman [60] experimental
results proved SVM could be used with a high degree of precision in predicting oil
prices. Bao et al. [59] presented a comparative study of recursive and direct strategies of
multi-step ahead prediction for both WTI and Brent crude oil spot prices with support
vector regression. As compared to results obtained through benchmark ARMA and
Random Walk models for crude oil price prediction, He et al. [61] confirmed the
superiority of the proposed slantlet denoising algorithm based on SVR model. Zhu
[62] formulated a two-stage structure for modelling oil future prices by partitioning
the whole input data space into mutually exclusive regions by K-mean clustering
algorithm and then corresponding SVM models. Table 7showed that each study have
preprocessed raw price data either by scaling range, return transformation or by cluster
classifier.
Most of the authors have used WTI as benchmark oil price data in their studies.
An important research gap in selection of input variables through judgemental criteria
or by literature review is highlighted under this category. It is evident from Table 8
that most of the authors have compared SVM with linear models. Xie et al. [67] has
shown SVM model performed better than back-propagation neural networks. The
model proposed by Zhu [62] has shown better performance in terms of MSE, MAE
and MAPE compared to standard SVM model. Radial basis kernel function is the most
popular choice among researchers for a price forecast problem as seen from Table 9.
The values for epsilon, cost and gamma vary across different studies. He et al. used
gradient search method to set appropriate model parameters [61,63]. Xie et al. [67]
and He et al. [63] have used directional statistics as performance criterion to compare
their respective models with traditional stochastic or regression models.
3.3 Genetically evolved models
Kaboudan [68] performed short term monthly forecasting of crude oil price using
genetic programming (GP) and neural networks. The study presented that GP can
produce impressive one-month ahead forecast compared to that by Random Walk and
ANN. This GP based oil price forecasting framework by Kaboudan [68] is considered
as benchmark for comparison by Amin-Naseri [69].
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Artificial intelligence methods for oil price forecasting...
Tab le 8 Forecasting performance comparison of support vector regression models
References Training
data (%)
Testing
data (%)
Forecast
horizon
Comparison with
other models
Level of accuracy
[59] 71 29 4, 8, 12 WA DirS, RecS RMSE: 5.05–52.11;
MAPE: 10.23
[60] 50 50 1 WA – PR: 81.27 %
[61] 60 40 – ARMA, RW MSE: 4.65
[62] 95 5 – S-SVM MSE: 1.37; MAE:
0.95; MAPE: 1.15
[63] 60 40 – RW, ARMA MSE: 8.74; DS:
53.07 %
[67] 88 12 1 MA ARIMA, BPNN RMSE: 2.19; DS:
70.83 %
Tab le 9 Support vector regression model architecture
References Kernel
function
Epsilon Cost Gamma Model
parameters
[59]RB0.01 – – –
[60] RB – 2965820 0.001953 2−15 to 215
[61]RB6.64 ×10−16 24.25 27.86 GSM
[62]RB– – – [0,1]
[63]RB7.81 ×10−30.5−83.90 ×10−3GSM
[67]RB– – – –
Tab le 1 0 Data characteristics, preprocessing technique, input variables & its selection method
References Oil
market
Time
scale
Input variable Input
selection
Preprocessing
[68] SP M V22, V3, V5, V15 TE –
[70]WTI;BrD– TE–
Xiao et al. [70] combined transfer learning techniques with analog complexing and
genetic algorithm for crude oil price forecasting. However, there is no information as
to how the input variables are selected in studies mentioned in Table 10. According to
Table 11,Xiaoetal.[70] showed that genetically evolved models performed better in
comparison to neural networks and ARIMA family models based on MSE,
RMSE and directional statistics. There is lack of information with respect to pre-
processing technique and fitness function used by authors as shown in Table 12.The
new combined models based on genetic algorithm with neural networks and that with
SVM are discussed in Sect. 3.5.
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N. Sehgal, K. K. Pandey
Tab le 1 1 Forecasting
performance comparison of
genetically evolved models
References Forecast
horizon
Comparison with
other models
Level of accuracy
[68] 1–12 MA ANN; RW MSE: 0.24–1.85;
[70] 1 TPA ARIMA; ANN;
GPMGA; AC
RMSE: 1.0691;
DS: 79.02
Tab le 1 2 Genetically evolved
model’s architecture References Cross-over
probability
Mutation
probability
Fitness
function
[68] 0.02 0.06 –
[70] 0.9 0.05 –
3.4 Wavelet-based models
A wavelet-based prediction model is proposed to provide forecast over 1–4 months’
horizon and to compare with future oil price data by Yousuf [71]. He et al. [7]intro-
duced the wavelet decomposed ensemble model to analyse dynamic changing nature
of underlying oil market structure. This study found that hybrid version comprising of
wavelet with neural networks is more appealing to researchers as compared to single
wavelet based model.
3.5 Hybrid models
3.5.1 Genetic Algorithm and Neural Network
Amin-Naseri [69] proposed a hybrid artificial intelligence model combining local
approximation techniques with genetically evolved neural network. The author used
Hannan-Quinn info criterion (HQIC) as fitness function and set number of hidden
neurons in range from 1–30. The performance of the model was evaluated with three
competing frameworks (STEO, KAB and WANG) for oil price forecasting. The pro-
posed model has performed well in terms on MSE, RMSE and directional statistics,
and has found to be effectively mapping the non-linearity and non-normality present
in crude oil price data. The proposed model has been considered as benchmark model
for comparison by Alexandridis and Livanis [72]. Fan et al. [73] presented General-
ized Pattern Matching based on Genetic Algorithm (GPMGA) to predict future prices.
GPMGA overcomes some limitations of Elman Networks and Pattern Modelling in
Recognition System (PRMS) approach for multi-step prediction of oil prices.
As evident from Table 13, authors have preprocessed raw price data to achieve high
level of accuracy. Authors have preferred autocorrelation function and partial auto-
correlation function to determine the optimal number of lags for model identification
and estimation.
The review states that genetically evolved neural networks are superior to other
competitive models as mentioned in Table 14. The studies under this category have
utilize sigmoid function as activation function. The number of neurons in hidden layers
varies as seen from Table 15.
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Artificial intelligence methods for oil price forecasting...
Tab le 1 3 Data characteristics, preprocessing technique, input variables & its selection method
References Oil market Time
scale
Input
variable
Input
selection
Preprocessing
[69]FP MV22PACFCC
[73] WTI; Br D V22 GT; ACF Standardization
Tab le 1 4 Forecasting performance comparison of Genetic Algorithm and Neural Network models
References Training
data (%)
Testing
data (%)
Forecast
horizon
Comparison with
other models
Level of accuracy
[69] 95 5 – STEO; KAB; WANG MSE: 0.90–9.10;
RMSE: 0.95–3.02;
DS: 71–81 %
[73] 99 1 1 MA PRMS; ENN RMSE: 1.57–2.43
Tab le 1 5 Genetic Algorithm and Neural Networks model’s architecture
References Model
type
Learning
Algorithm
Hidden
neurons
Activation
function
Fitness
function
Cross-over
probability
Mutation
probability
[69] MLP LM; GD 1–30 LSig HQIC 0.9 0.01
[73] RNN BP TE TSig – 0.9 0.09
3.5.2 Wavelet and Neural Network
Mingming et al. [74] proposed a multiple wavelet recurrent neural network based
hybrid method for international crude oil prices. This model utilized wavelet analysis
to capture multi scale data characteristics, while designing an appropriate recurrent
neural network to predict oil prices at different time scales, followed by a standard
back-propagation neural network to combine these independent forecasts. He et al.
[75] proposed an ensemble approach incorporating wavelet and feed-forward neural
network for estimating VaR in crude oil market to further improve modelling accu-
racy and reliability of three oil markets: WTI, Brent and Dubai. Jinliang et al. [17]
decomposed crude oil price time series into several trend and random component. For
higher prediction accuracy, the trend component of oil prices is predicted with Boltz-
mann neural network and the random component is predicted with Gaussian kernel
density function as shown in Table 16. Jammazi and Aloui [76] examined a short
term forecasting of monthly WTI prices with different input-hidden nodes combina-
tions and three types of activation functions. The results highlighted combination of
Harr A Trous wavelet function with back propagation neural network as a promising
forecasting tool. Pang et al. [43] proposed to predict monthly oil prices using OECD
inventory level as independent variable, and used wavelet theory based feed forward
neural network to model the non-linear relationship between oil prices and inventory.
123
N. Sehgal, K. K. Pandey
Tab le 1 6 Wavelet and Neural
networks model’s architecture References Model type Learning
algorithm
Hidden
neuron
Activation
function
[74] RNN; MLP BP TE LSig
[75] MLP LM 6 LSig
[17]BNN– ––
[76] MLP BP TE BiP Sig
[43]MLPGD 8–
[77] RBF – 4 –
[72]WNN– 1–
Tab le 1 7 Data characteristics, preprocessing technique, input variables and its selection method
References Oil
market
Time
scale
Input variable Input
selection
Preprocessing Wavelet function
[74] WTI, Br A V22, V42 JC WT Db
[75] WTI, Br, Du W V22 JC RT, WT HaT; Db; Coiflet
[17] WTI M V22, V42 JC WT Db
[76] WTI M V22 JC WT HaT
[43] WTI M V22, V20,
V45, V46
JC WT Morlet
[77] Br M V22 JC WT, SR Db
[72] WTI M V22, V43, V3 CA WT –
The proposed model achieved lower RMSE, MAPE and MAE in comparison to both
linear and non-linear relative inventory models.
Qunli et al. [77] decomposed the original price sequence successfully using discrete
wavelet transform as input layer of radial basis function neural network. Alexandridis
and Livanis [72] used wavelet neural network to forecast monthly WTI crude oil spot
prices using price lags, world crude oil production and the producer price index for
petroleum as explanatory variables.
Out of seven articles listed in Tables 17,18 only one author has emphasized on
selecting input variables based on correlation analysis. Correlation analysis is a mea-
sure of linear relationship between variables but macroeconomic variables exhibit
non-linear relationship with oil prices. Therefore, there is a requirement to develop
a method for identifying significant input indicators based on non-linear relationship
that exists between variables. It can be observed that Daubechies has been adopted by
most researchers as a wavelet function. The prediction accuracy of combined models
is evaluated with linear ARMA family, non-linear neural networks, STEO, WANG
and AMIN models.
3.5.3 Fuzzy Neural Network
Panella et al. [78] favoured the quality of forecasting accuracy based on neurofuzzy
approach (adaptive neuro-fuzzy inference system) in comparison to other linear and
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Artificial intelligence methods for oil price forecasting...
Tab le 1 8 Forecasting performance comparison of Wavelet and Neural Network models
References Training
data (%)
Testing
data (%)
Forecast horizon Comparison with
other models
Level of accuracy
[74] 70 30 4–8; 8–16; 16–32 YA – MSE: 3.88–4.06
[75] 36 24 – ARMA-GARCH MSE:
0.0059–0.0131
[17]– – – – –
[76] 80 20 19 MA MLP MSE: 3.89; HR:
73 %; R2: 0.997
[43] 82 18 1 MA, 2MA, 3MA L-RIM, NL-RIM RMSE:1.486;
MAE:1.073;
MAPE:2.263
[77] 68 32 – – SSE: 6.16 ×10−5
[72] 56 50 1MA, 3MA, 6MA WANG, AMIN, STEO MSE: 2.05; MAE:
1.02; Max AE:
7.36
Tab le 1 9 Data characteristics, preprocessing technique, input variables and its selection method
References Oil
market
Time
scale
Input variable Input
selection
Preprocessing
[78]WTI;BrDV22 JCLT;CC
[79]WTIDV22 JCSMP
[64] WTI A V11, V4, V21, V10 JC –
neural network models. Ghaffari and Zare [79] presented a method based on soft
computing approaches to forecast WTI crude oil spot prices for one-month ahead
forecast horizon. Azadeh et al. [64] used oil supply, surplus capacity, Non-OECD
consumption, U.S. refinery capacity and crude oil distillation capacity as input variable
for designing a flexible ANN-FR algorithm to model noisy and complex oil prices.
Further, the ANOVA and Duncan multiple range test are used to test the significance
of the forecast obtained from ANN and FR models. Table 19 indicates that the input
variables has been selected based on judgemental criterion and no prior selection
method has been applied.
Ghaffari [79] explored the possibility of smoothing procedure as preprocessing
tool to explore the pattern of oil prices while Panella et al. [78] incorporated both
log transformation and cluster classification. As clear from Table 20, the prediction
accuracy of proposed neuro-fuzzy model by Panella et al. [78] has been compared
with linear and non-linear models on the basis of noise-to-signal ratios.
Ghaffari [79] has compared the results of their respective models with and without
smoothing procedure and observed that prediction quality in terms on percentage
of correct predictions has been improved by smoothing oil price data. It is evident
from Table 21 that researchers preferred to develop fuzzy model by adopting Takagi–
Sugeno–Kang as fuzzy inference system and Gaussian as membership function. The
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N. Sehgal, K. K. Pandey
Tab le 2 0 Forecasting performance comparison of Fuzzy Neural Network models
References Training
data (%)
Testing
data (%)
Forecast
horizon
Comparison with
other models
Level of accuracy
[78] 67 33 1 SA LSE; RBF; MoGNN NSR: −46 to −24
[79] 80 20 30 DA WSP PCP: 68.18–70.09
[64] 80 20 – ANN, FR MAPE: 0.035
Tab le 2 1 Fuzzy Neural Networks model’s architecture
References Model
type
Learning
Algorithm
Hidden
neurons
Fuzzy inference
system
Membership
function
[78] MLP LSE + BP TE TSK Gaussian
[79] MLP LSE + GD TE TSK Gaussian
[64] MLP BFGS; BR;
BLR; GDX;
LM
TE – Gaussian
multi-layered perceptron neural network is the most popular among researchers for
oil price forecasting under this category of neuro-fuzzy approach. Azadeh [64] model
architecture includes MLP along with five variant of learning algorithm to improve
the forecasting performance and achieved MAPE as low as 0.035.
3.5.4 Decomposition based Neural Network
Yu et al. [80] proposed a “decomposition-and-ensemble” strategy using EMD-based
NN ensemble learning model to predict oil prices. Empirical mode decomposition is
proposed to decompose oil price data into eleven intrinsic mode functions. Xiong et al.
[81] evaluated the performance of EMD-based feed-forward neural network frame-
work incorporating slope-based method for oil price forecasting with three leading
strategies: direct, iterative and multiple-input multiple-output (MIMO).
Xiong et al. [81] used Symmetric MAPE (SMAPE) as a forecasting performance
criterion to evaluate the performance of EMD based neural network. In Table 22,
partial mutual information is used to examine the relationship between historic prices
and oil prices together with forward backward selection and delta test for model
identification and estimation. Multi-layered perceptron neural network is the most
widely used neural network architecture by researchers for hybrid models as evident
from Tables 23,24.
3.5.5 Support vector and Genetic Algorithm
Guo [82] improved traditional SVR forecast precision by using genetic algorithm opti-
mized parameter of SVR in accordance with the training data. The model is found to
be effective in mapping the complexities of oil price series. Gabralla [83] investigated
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Artificial intelligence methods for oil price forecasting...
Tab le 2 2 Data characteristics, preprocessing technique, input variables and its selection method
References Oil
market
Time
scale
Input
variable
Input
selection
Preprocessing
[80]WTI;BrDV22JC –
[81] WTI W V22 PMI; DT; FBS SR
Tab le 2 3 Forecasting performance comparison of decomposition based Neural Network models
References Training
set (%)
Testing
set (%)
Forecast
horizon
Comparison with
other models
Level of accuracy
[80] 72 28 1 DA ARIMA; MLP RMSE: 0.273–0.225;
DS: 86.99–87.81
[81] 67 33 4 WA MLP SMAPE: 2.28–8.15;
MAE: 0.81–1.28;
DS:54–87
Tab le 2 4 Decomposition based Neural Networks model’s architecture
References Model
type
Learning
Algorithm
Hidden
neurons
Activation
function
Decomposition
method
No. of
IMF’s
[80] MLP; ALNN BP – Lgs; Lin EMD 11
[81] MLP LM 15 – EMD –
performance of two different algorithms for feature selection together with several
machine learning methods (IBL, KStar and SMOreg) for oil price prediction.
4 Limitations
There is no solitary indicator driving crude oil prices. The output is based on how
much information is contained in the set of input variables selected for the study.
There are many studies that had examined the relationship between oil prices and
macroeconomic variables but there seem no consensus on the extent to which these
macroeconomic variables drive oil prices. Existing studies of predicting oil prices have
accounted for non-linearity, non-stationary and time-varying structure of the oil prices
but have seldom focus on selecting significant features with high predicting power.
The empirical literature is very far from any consensus about selecting the appropriate
features/indicators that explains the characteristics of oil market. In most of the studies,
the design of input vector for oil price forecasting model is carried out on judgemental
criteria or trial and error procedures. Little attention is paid on selecting influential
factors and more on assessing new techniques for oil price forecasting.
The effect of input variables is considered to constantly driving oil prices in different
studies. There is shift in the influence of input variables subject to happening of any
123
N. Sehgal, K. K. Pandey
geopolitical and economic events in a given time-period but there is no literature
available that highlights this point. Predicted oil prices are dependent on short term
macroeconomic indicators whose effects are subject to structural changes. Few studies
have been carried out using artificial intelligent models to forecast complex oil price
series as compared to its application in diverse fields. Recently, researchers suggest
integration of neural networks with traditional methods as support vector regression,
genetic algorithms or wavelets by mean of hybrid approach to overcome limitations
of single models.
5 Concluding remark
Off late, artificial intelligent models are being extensively used to capture unknown
or too complex structures in time series. This review focussed on artificial intelligent
based oil price forecasting models and attempted to provide in-depth review based
on the following parameters: (i) type of model, (ii) input variables, (ii) input variable
selection method, (iv) data characteristics, (v) forecasting performance, and (vi) model
architecture. It enlisted the numerous key indicators used as input variables in artificial
intelligent based oil price forecasting models and attempted to highlight a serious issue
of selecting input variables based on judgemental or trial and error basis.
This review concludes that there is no single indicators driving oil prices and there
is need to identify the relevant input variables for oil price predictions. Multi-layered
perceptron neural network is most widely used by researchers for price forecasting.
Recently, researchers suggests integration of neural networks with traditional meth-
ods as support vector regression, genetic algorithms or wavelets by mean of hybrid
approach to overcome limitations of single models. It also highlighted that effect of
factors is constraint to happening of geopolitical and economic events. The research
gap highlighted to develop a robust feature selection method that can account for
non-linearity and time-varying structure of oil prices.
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