A review on artificial intelligence methodologies for the forecasting of crude oil price

Intelligent Automation & Soft Computing

ISSN: 1079-8587 (Print) 2326-005X (Online) Journal homepage: http://www.tandfonline.com/loi/tasj20

A Review on Artificial Intelligence Methodologies for the Forecasting of Crude Oil Price Haruna Chiroma, Sameem Abdul-kareem, Ahmad Shukri Mohd Noor, Adamu I. Abubakar, Nader Sohrabi Safa, Liyana Shuib, Mukhtar Fatihu Hamza, Abdulsalam Ya’u Gital & Tutut Herawan To cite this article: Haruna Chiroma, Sameem Abdul-kareem, Ahmad Shukri Mohd Noor, Adamu I. Abubakar, Nader Sohrabi Safa, Liyana Shuib, Mukhtar Fatihu Hamza, Abdulsalam Ya’u Gital & Tutut Herawan (2016): A Review on Artificial Intelligence Methodologies for the Forecasting of Crude Oil Price, Intelligent Automation & Soft Computing To link to this article: http://dx.doi.org/10.1080/10798587.2015.1092338

Published online: 11 Jan 2016.

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Date: 11 January 2016, At: 02:32

Intelligent Automation and Soft Computing, 2016 http://dx.doi.org/10.1080/10798587.2015.1092338

A Review on Artificial Intelligence Methodologies for the Forecasting of Crude Oil Price Haruna Chiromaa,h, Sameem Abdul-kareema, Ahmad Shukri Mohd Noorb, Adamu I. Abubakarc, Nader Sohrabi Safad, Liyana Shuibe, Mukhtar Fatihu Hamzaf, Abdulsalam Ya’u Gitalg and Tutut Herawane Department of Artificial Intelligence, University of Malaya, 50603 Pantai Valley, Kuala Lumpur, Malaysia; bSchool of Informatics and Applied Mathematics, Universiti Malaysia Terengganu 21030, Kuala Terengganu, Malaysia; cKuliyyah of Information and Communication Technology, International Islamic University Malaysia, Gombak, Kuala Lumpur, Malaysia; dCenter for Research in Information and Cyber Security, School of ICT, Nelson Mandela Metropolitan University, Port Elizabeth, South Africa; eDepartment of Information Systems, University of Malaya 50603, Pantai Valley, Kuala Lumpur, Malaysia; fDepartment of Mechatronics Engineering, Faculty of Engineering, Bayero University, Kano, Nigeria; gMathematical Science Programme, Abubakar Tafawa Balewa University, Bauchi, Nigeria; hComputer Science Department, Federal College of Education (Technical), Gombe, Nigeria

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a

ABSTRACT

When crude oil prices began to escalate in the 1970s, conventional methods were the predominant methods used in forecasting oil pricing. These methods can no longer be used to tackle the nonlinear, chaotic, non-stationary, volatile, and complex nature of crude oil prices, because of the methods’ linearity. To address the methodological limitations, computational intelligence techniques and more recently, hybrid intelligent systems have been deployed. In this paper, we present an extensive review of the existing research that has been conducted on applications of computational intelligence algorithms to crude oil price forecasting. Analysis and synthesis of published research in this domain, limitations and strengths of existing studies are provided. This paper finds that conventional methods are still relevant in the domain of crude oil price forecasting and the integration of wavelet analysis and computational intelligence techniques is attracting unprecedented interest from scholars in the domain of crude oil price forecasting. We intend for researchers to use this review as a starting point for further advancement, as well as an exploration of other techniques that have received little or no attention from researchers. Energy demand and supply projection can effectively be tackled with accurate forecasting of crude oil price, which can create stability in the oil market.

1. Introduction It is observed by Hamilton (2011) that crises in the crude oil market cause price volatility, which has direct and indirect negative effects on the global economy and inflicts suffering on communities across the globe. The effects of crude oil volatility have no geographical boundary, because there is no restriction to a specific country or region of the world. Developed economies, such as those of the US and Britain, and developing economies, such as those of Malaysia, Argentina, and South Africa, have been affected by crude oil price volatility.

Even highly indebted poor countries, such as Uganda, GuineaBissau, Ukraine, Armenia, and the Kyrgyz Republic, among others, that do not contribute a significant amount of the world GDP, are highly affected by oil price volatility. The volatility of crude oil price is often times triggered by unexpected events, such as the Iran-Iraq War, the Iranian Revolution, the First Gulf War, Venezuelan unrest, and the Second Gulf War. The forecasting of crude oil price would be a significant contribution to overcoming its negative impact (Hamilton, 2011). In the area of crude oil, forecasting remains one of the greatest challenges, and research has been active over the years (He, Xie, Chen, & Lai, 2009; Kulkarni & Haidar, 2009). In the 1970s, when crude oil prices started escalating to be higher than ever seen before, conventional econometric, statistical and mathematical models were the predominant methods that were used in the

CONTACT Haruna Chiroma © 2016 TSI® Press

hchiroma@acm.org

KEYWORDS

Crude oil price; Genetic algorithms; Neural networks; Hybrid intelligent systems; Individual intelligent systems; Computational intelligence techniques

forecasting of crude oil prices (Kaboudan, 2001). These methods can effectively solve only linear or near-linear problems and some complex nonlinear time-varying problems in a limited way, which cannot meet practical needs. These limitations have triggered growing interest in computational intelligence techniques due to their ability to handle complex problems more efficiently when compared to conventional methods (Yu et al., 2008b).

We intend to provide a clear perspective with a broad and in-depth critical review of the research studies in this domain, and we intend for researchers to use this critical review as a starting point for further advancement as well as an exploration of other techniques that have received little or no attention from researchers. These stated objectives were the driving force that motivated this critical review article.

It can be stated that the review conducted in (Chiroma, Abdulkareem, Abubakar, & Mohammed, 2013; Gabralla & Abraham, 2013) is the closest to our study, because the study reported a systematic literature review on the applications of computational intelligence techniques to the crude oil price forecast, which differ from the critical review presented in this review. The contributions of our study are in four aspects: i. The study revealed artificial intelligence techniques that have received little or no attention in the domain of crude oil price forecasting.

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ii.  This paper finds that conventional methods are still relevant in the domain of crude oil price forecasting and the integration of wavelet analysis and computational intelligence techniques is attracting unprecedented interest from scholars in the domain of crude oil price forecasting. iii.  The study revealed a review of the previous studies to ascertain the methodologies used in predicting the oil price using the computational and hybrid technologies. iv.  The study classified the researches according to their strengths, weaknesses, frequency of data collection (advantages and disadvantages), and methods of comparison with propose approach.

The rest of this paper is organized as follows. Section 2 describes the review on computational intelligence techniques.

Section 3 describes review on hybrid intelligent systems vs. individual intelligent systems. Section 4 describes applications of computational intelligence techniques in forecasting crude oil price. Finally, the conclusion of this work is described in Section 5.

Figure 1. Structure of an FFNN.

when the size of the training data increases (Cigizoglu & Alp, 2006). Other types of neural networks discussed in the literature are described as follows: 2.2.  Support Vector Machine According to Jin (2005), statistical learning theory is responsible for inspiring the theory of the support vector machine.

In support vector machines, local minima do not exist during training. The generalization ability of support vector machines ( ) does not rely on the space dimension. Given z samples ri , si , where i = 1, 2, 3, ⋯, z, building the model is formulated as the minimization of the ξ–insensitive loss function:

2.  Computational Intelligence Techniques In this section, the basic concepts of computational intelligence techniques applied in the domain of crude oil price forecasting are briefly introduced so that any reader can understand how these techniques operate and how they can attain their main goals.

2.1.  Neural Networks Neural networks are a group of algorithms that were inspired from human biological neural systems. The structure of a neural network is composed of input, hidden, and output layers, with neurons distributed across the layers, as shown in Figure 1. A systematic framework for the chosen number of hidden layer neurons is not in existence. The predominant technique applied in determining the appropriate number of hidden-layer neurons is trial and error.

The network is trained using training algorithms, such as back-propagation learning algorithms. The training phase minimizes the error in the network weights, while considering the training samples. The training process is terminated when the following occurs: The maximum iteration is at its peak; the gradient performance falls below a threshold, or the error is minimized to a required value (Cheng & Li, 2008).

The neural network described is a feed-forward neural network (FFNN). Other categories of neural networks exist in the literature, such as the group method of data handling for a neural network. In the group method of data handling for a neural network, every neuron distributed in the network layers is connected by a quadratic polynomial. These neurons produce different sets of neurons in the subsequent layer (Ardalan, Eslami, & Nariman-Zadeh, 2009). Unlike an FFNN, the group method of data handling for a neural network has no possibility of being stuck in a local minimum, and significant inputs and the structure of the network are automatically configured (Abdel-Aal, 2008). Another type of neural network is a generalized regression neural network (GRNN). Unlike FFNN back-propagation algorithm, GRNNs do not require iterative training. The algorithms approximate functions directly from the training data. The approximation error diminishes to zero

z

z = k2 + c.

{ } 1∑ max ||si − f (ri )|| − đ?œ€ z i=1

(1)

In Equation 1, Îľ, c, and f represent the tolerance error, regularization constant, and regularization function, respectively. In Equation 1, f is the function to be approximated.

f (r) = k.r + d, k, r ∈ Rr , b ∈ R

(2)

The equivalent of Equation (2) in a constrained optimization problem is given as: z

ďż˝ 1 1 �� đ?œ‰i + đ?œ‰i∗ Minimize ‖k2 ‖ + c.

2 z i=1 Subject to

si −

(( ) ) k.ri + d − si ≤∈ +đ?œ‰i

(( ) ) k.ri + d ≤∈ +đ?œ‰i∗ ,

where đ?œ‰i , đ?œ‰i∗ ≼ 0, i = 1, 2, 3, ‌ , z.

(3)

(4) (5) (6)

Support vector machines have been applied to solve function approximation, classification, predictions, and regression, among others. Suykens and Vandewalle (1999) developed a least-squares support vector machine, which has the ability to solve sets of linear equations. It considers equality-type constraints, unlike the standard support vector machine, which solves quadratic or linear programming problems that have inequality constraints. The modeling performance of the leastsquares support vector machine is good, and it is relatively easy to train. In the cluster support vector machine developed in (Qi & Zhang, 2009), the training data are split into pairwise separate clusters, and every cluster contains either support vectors or non-support vectors.

2.3.  Radial Basis Function Neural Network Radial basis function neural networks (RBFNNs) are composed of three layers of neurons (the input, hidden, and output

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whereas hidden neurons receive inputs from both internal and external neurons (Elman, 1990).

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2.5. Genetic Algorithms A genetic algorithm (GA) is a search algorithm that is based on natural selection and is inspired by Darwin’s theory of natural evolution. The algorithms start by generating an initial population that is believed to have the optimal solutions. The implementation of the GAs is based on three critical operators; selection, crossover, and mutation. During selection, chromosomes with the best performances are selected, and those that performed poorly are eliminated from the mating pool. This process is iteratively executed until the best chromosome emerges as the optimal solution to the problem (Shakeri, Alibeigloo, & Morowat, 2005). The entire process of implementing a GA is represented in Figure 3.

Figure 2. Recurrent Neural Networks.

2.6. Genetic Programming layers). The hidden layer is the only layer that contains the radial basis functions. The activation function of the hidden layer is a radially symmetric basis function. Inputs into the network are passed directly to the hidden layer neurons without undergoing computation with the values of the initial weights.

The distance between the input vector and the center of the radial function is measured by the hidden layer neurons (Wang, Chen, & Yan, 2013).

2.4. Recurrent Neural Network Recurrent neural networks (RNNs) are structurally more complex than FFNNs, because recurrent inputs at the hidden layer are connected to the intermediate layer. Figure 2 presents a typical representation of a recurrent network that has five input neurons, five internal input neurons, five hidden layer neurons, and two output neurons.

Neurons distributed in the network layers are connected through weights and are iteratively updated using the widely applicable back-propagation learning algorithms (Quek, Pasquier, & Kumar, 2008). The internal neurons in the context units accept their inputs from neurons in the hidden layers,

Figure 3. GA Operations Processing.

Genetic programming (GP) is an evolutionary method that implements GAs; with GPs, the processes that are used for problem optimization are similar to those in the GAs. The initial population in GP is computer programs that are encoded using a syntax tree. Functions and arguments are represented by internal nodes. Branches are functions to these arguments, and variables, constants, and functions without arguments are represented by leaves of the tree, which are referred to as terminals (Searson, 2005).

2.7. Evolutionary Immune Clustering Algorithm The artificial immune system algorithms are a set of computational intelligence algorithms that emerged based on inspiration from biological immune systems, which guard the human body from external attacks by microorganisms and other similar infections; these algorithms can be applied to problem solving. Programming in artificial immune systems is designed by using antibodies and antigens, similar to real biological immune systems, which have genetic operators that cause the chromosomes to adapt during the entire process. The main stages of immune programming constitute vaccination

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Figure 5. Operations of Fuzzy Logic Systems.

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Figure 4. Expert Systems.

and immune selection. During implementation, the considered problem is comprehensively analyzed in detail (antigen), whereas fundamental features of the problem are to be established and taken as vaccinations. Subsequently, the features are removed into an antibody. At the final stage, the antibody creates the foundation for the operation of the immune operator to generate fresh chromosomes. The iterations continue until an optimal solution is found in the search space (De Castro & Timmis, 2002).

2.8. Expert Systems Expert systems are a set of systems that are composed of a database, inference engine, user interface, explanation mechanism, and knowledge base, as shown in Figure 4.

Domain knowledge (e.g., crude oil) is critical for solving a problem. The knowledge is represented in the form of rules that specify conditions and actions to perform based on the executed conditions. The database in expert systems stores the facts that match with the set of rules contained in the knowledge base. The inference engine provides an interface between the database and knowledge base, thereby making the systems act intelligently and produce results. The explanation mechanism enables a user to ask why certain conclusions were reached. Justification of the advice or analysis provided by the systems is thereby explained by this mechanism. The point of interaction between a human and the expert systems is provided by the user interface (Negnevitsky, 2005).

2.9. Fuzzy Logic In Chen & Pham (2001), logic is described as methods that are composed of cognitive human principles. The principle that guides classical logic is the preposition of true or false. The variable can only be either true or false, but never true and false simultaneously. In fuzzy logic, imprecise data and vague statements are accepted as inputs, and a decision is yielded.

Figure 5 is a complete representation of fuzzy logic systems modified from (Zalloi, 2009).

For example, Zalloi (2009) has applied fuzzy rules to define rules that were based on unexpected events in the crude oil price movement, as follows: If demand = small, inventory = high, speculation = no, oil company merger = no, then crude oil price moves down.

2.10. Fuzzy Regression Fuzzy regression is a modification of the conventional regression model used to predict dependent variables based on

independent variables. Fuzzy regression is most suitable in a situation where datasets are relatively inadequate and vague and when descriptive variables are interrelated in a fuzzy manner, i.e., qualitatively and with uncertainty. Additionally, the fuzzy regression model performs well with incomplete data and requires a limited number of observations, unlike the cases of other models that have been applied in the prediction task (Azadeh, Moghaddam, Khakzad, & Ebrahimipour, 2012; Enke & Mehdiyev, 2013).

3. Hybrid Intelligent Systems vs. Individual Intelligent Systems Computational intelligence techniques all have limitations (Abraham, Corchado, & Corchado, 2009), despite their effectiveness in solving linear, nonlinear, chaotic, and complex problems, such as the forecasting of price in financial time series. For example, the most widely used neural network is FFNN back-propagation, which could possibly become stuck in local minima and over-fit the training data. There is no ideal framework for determining the optimal structure of the network and the selection of the initial training parameters.

Researchers employ cumbersome trial-and-error techniques to determine the optimal structure and selection of the parameters. Neural networks depend on past events, which might not re-occur (Bahrammirzaee, 2010). The GA can have the following limitations: The GA fitness function is difficult to classify; the problem representation definition in the GA can be difficult; the occurrence of early convergence; no specific way to determine parameters, such as the population size, mutation rate, crossover rate, and number of generations; difficulty in fusing precise information in a problem; performs poorly in detecting local optima; does not have any real terminator; and difficulty in locating the precise global optimum (Sivanandam & Deepa, 2007).

In expert systems, the results could not be improved from experience; the system only follows if/then rules. Additionally, the expert systems lack the capability of identifying nonlinear relationships (Bahrammirzaee, 2010). Expert systems do not tolerate missing or erroneous values. They execute rules to perform actions, and the rules must be stored in a knowledge base before they can be handled. The knowledge base is manually updated by a knowledge engineer, starting from human expert knowledge, when there is a change in the expertise; as a result, the systems can effectively handle changes from increased experience (Niculescu, 2003). Fuzzy systems lack the capability of learning input data; human language is used to represent the input and output of the systems. Thus, incomplete or wrong rules cannot be handled well by fuzzy systems.

Tuning of the systems is not a direct task (Bunke & Kandel, 2000). Limitations of individual techniques are eliminated

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through hybridization to achieve a synergetic effectiveness in the design of an intelligent system (Abraham et al., 2009).

Hybrid intelligent systems (HISs) are systems that fuse two or more computational intelligence techniques to solve complex and challenging problems. HISs are created to; enhance performance, provide opportunities for multiple applications of tasks, and improve the capability of handling multiple functions (Bunke & Kandel, 2000). For example, Rast (1997) hybridized neural networks and fuzzy systems, Li, Zhang, Wong, and Qin (2009) fused fuzzy systems, neural networks, and Gao, Zhou, Gao, and Shi (2006) integrated a neural network, GA, and swarm particle optimization. However, individual intelligent systems (IISs) are standalone intelligent techniques that are applied for problem solving without fusion with other intelligent techniques, and their performances are inferior to HISs in terms of accuracy (Bahrammirzaee, Ghatari, Ahmadi, & Madani, 2011; Verikas, Kalsyte, Bacauskiene, & Gelzinis, 2010).

For example, an individual FFNN is used to forecast crude oil price in (Movagharnejad, Mehdizadeh, Banihashemi, & Kordkheili, 2011).

4.  Applications of Computational Intelligence Techniques in Forecasting Crude Oil Price As stated earlier, crude oil prices have mainly been forecasted with conventional methods. For example, Barone-Adesi, Bourgoin, and Giannopoulos (1998) forecast crude oil price using a semi-parametric technique. Morana (2001) propose a semi-parametric statistical tool to forecast crude oil price using the autoregressive conditional heteroskedasticity (GARCH) properties of crude oil as independent variables. Ye, Zyren, and Shore (2006) proposed a model with Econometrics to forecast crude oil price based on Organization for Economic Co-operation and Development (OECD) petroleum inventories. Similarly, Abramson and Finizza (1991) proposed a probabilistic model to forecast the price of crude oil. Mirmirani and Li (2004) predicted crude oil price using a vector auto-regression model. Documented evidence in (Bahrammirzaee, 2010), reveals that computational intelligence techniques are superior to conventional methodologies in forecasting chaotic financial time series, but there are a few cases in which conventional methods perform better than intelligent techniques.

However, these conventional methods are still relevant in the domain of crude oil price forecasting. For example, Shouyang et al. (2005) integrate the Box-Jenkins forecasting model into a HIS to model linear constituents of the crude oil price time series. Kulkar and Haidar (Kulkarni & Haidar, 2009) used a moving average to eliminate noise in a crude oil price time series during data pre-processing. Liu, Bai, and Li (2007) define a Markov chain as input to a fuzzy neural network, to improve the forecasting accuracy of crude oil price. Phichhang, and Wang (2011) hybridized GARCH and neural networks to model the volatility of crude oil price. Apart from the statistical methods, there are other techniques that are gradually gaining popularity in this domain, especially wavelets, which have drawn unprecedented interest in their hybridization with computational intelligence techniques to improve the forecasting accuracy of crude oil price. According to Jammazi and Aloui (2012), wavelet analysis is an advancement in the area of harmonic analysis. Wavelets have the capability of projecting data into the time scale domain and conducting multi-scale analysis. Wavelets are also used to capture both smooth and low-frequency data and detailed and high-frequency data.

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Examples of studies where wavelet analysis is used in conjunction with intelligence techniques in various ways are as follows: Jammazi and Aloui (2012) decompose the non-stationary nature of crude oil data using wavelet analysis and then incorporated it into a neural network to forecast crude oil price.

Their propose neural network was found to perform better than the conventional back-propagation neural network. Mingming and Jinliang (2012) use wavelet analysis to capture multi-scale characteristics of crude oil price data, where RNN was used at the different scales to forecast price. The numerous forecast results were recombined by a back-propagation neural network to produce an ensemble forecast result. Bao, Zhang, Yu, Lai, and Wang (2007) applied wavelets to decompose crude oil data before applying a least square support vector machine to capture useful information at various scales. He, Yu, and Lai (2012) integrated wavelet analysis and a neural network to improve the reliability and accuracy of crude oil price forecasting during the modeling process. Pang et al. (2011) used wavelet analysis to decompose and incorporate several layers within the data.

They then applied a neural network to reduce estimation bias introduced by the wavelet analysis to improve the crude oil price forecast accuracy. Pang et al. (2011) applied a wavelet neural network to forecast crude oil price. The network uses orthogonal or continuous wavelets as an activation function instead of sigmoid activation, because it requires less iteration to converge. Jinliang, Mingming, and Mingxin (2009) used wavelets to decompose crude oil price data into approximate and random constituents. They then applied a neural network to forecast the approximate constituent, which represented the trend of the oil price. In (Ma & Wu, 2010), grey theory is used to reduce the randomization that exists in crude oil data. A rough set is used in Xu, Wang, Zhang, Zhang, and Wang 2007 to extract relevant factors that negatively impact crude oil price.

Fan, Liang, and Wei (2008) used generalized pattern matching to detect frequently occurring patterns in the historical data of crude oil price. In Wang et al. (2005), web-based text mining was used to retrieve relevant information that affects crude oil price volatility.

Computational intelligence techniques, such as neural networks, GAs, fuzzy logic, and, recently, HISs, Khan (1999) provide solutions to complex, nonlinear, non-stationary, and chaotic crude oil price forecasting. A summary of the research that has been conducted on the application of computational intelligence techniques to forecasting crude oil price is presented in Table 1 (Chiroma et al., 2013).

In Table 1, Column 2 indicated that an overwhelming majority of the studies in the literature used data that were extracted from the US Department of Energy. Other literature sources have experimental data from elsewhere, whereas a few do not disclose their source. West Texas Intermediate and Brent crude oil price are widely used as the benchmark for international crude oil price in research studies. West Texas Intermediate, Brent, and Dubai represent the major crude oil benchmarks for international oil pricing systems at present (Fattouh, 2010).

Crude oil price in other markets across the globe are formulated with reference to these price, thereby making these price dependent on the international benchmark (He et al. 2009).

Column 3 indicates that most studies collect voluminous historical data for their experiments. The study that has the largest amount of voluminous experimental data is (Mingming & Jinliang, 2012), followed by Sotoudeh and Farshad (2012), whereas in (Ma, 2009), only five months of historical data were used to build the forecasting model, which is the lowest

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Table 1. Summary of the Published Papers by Technique, Data Source, and Data Collection Period.

Reference Movagharnejad et al. (2011) Quek et al. (2008) Panella, Barcellona, Santucci, and D’Ecclesia (2011) Mingming and Jinliang (2012) Liu et al. (2007) Wang, Pan, and Liu (2012) Malik and Nasereddin (2006) Fernández (2006) Shambora and Rossiter (2007) Torban (2010)

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Lai et al. (2007) Mehdi (2009) Ma (2009) Shouyang et al. (2015) Xie, Yu, Xu, and Wang (2006) Lackes et al. (2009) Haidar et al. (2008) Zimberg (2008) Qi and Zhang (2009) Jinliang et al. (2009) Alizadeh and Mafinezhad (2010) Mingming, Jinliang, and Mingxin (2009) Chen and Qu (2011) Rast (2001) Moshiri and Foroutan (2006) Wang and Yang (2010)

Abdullah and Zeng (2010)

Source of Data American Petrochemical Institute and US Department of Energy Bloomberg and datastream US Department of Energy Not disclosed US Department of Energy Brent, West Texas Intermediate, Dubai, Daqing and Shengli Bureau of Labor Statistics & Bureau of Economic Analysis DataStream DataStream US Department of Energy Global Financial Data US Department of Energy and Mediteranean sidi kerir Iran New York harbor residual US Department of Energy Not disclosed Not disclosed US Department of Energy, http:// www.normanshistoricaldata.com and http://finance.yahoo.com US Department of Energy Not disclosed Not disclosed Not disclosed Not disclosed Not disclosed Not disclosed US Department of Energy Not disclosed

Period January, 2000 to April, 2010

Technique FFNN

2000 to 2002 2001 to 2010

RNN Fuzzy logic and FFNN

1945 to 2010 May 20, 1987 to August 30, 2006 January 2, 2003 to December 31, 2009

RNN Fuzzy logic, FFNN FFNN

January, 1947 to December, 2004.

FFNN

1994 to 2005 April 16, 1991 to December 31, 2003 First quarter of 1986 to fourth quarter of 2009 April 4, 1983 to June 30, 2006 January, 1989 up to January, 2009

FFNN and support vector machine FFNN FFNN

November 29, 2006 to 24 April, 2007

FFNN Fuzzy logic, expert systems and FFNN

January, 1970 to December, 2003 January, 1970 to December, 2003 1999 to 2006 1996 to Aug 2007

Evolutionary Immune clustering algorithms and RBFNN Expert systems and FFNN Support vector machine FFNN FFNN

1991 to 2003 Not specified by authors 1975 to 2009 20 months 1976 to 2009

Fuzzy logic and FFNN Cluster support vector machine FFNN GRNN FFNN

1996 to 2010 Not specified by authors 1983 to 2003 November 1, 2006 to October 31, 2007, May 1, 2005 to September 30, 2005, January 1, 2007 to May 30, 2007, December 1, 2000 to November 31, 2001, January 1, 2001 to June 31, 2001 and May 1, 2001 to October 30, 2001.

January, 1984 to February, 2009

FFNN Fuzzy logic and FFNN FFNN FFNN

FFNN

January 1, 1985 to October 12, 1992

FFNN

June 2, 1998 to November 30, 2000 January, 1997 to October, 2008 January, 1993 to December, 1998

Fuzzy logic and FFNN RBFNN GP and FFNN Least-squares support vector machine

Bao et al. (2007)

Online news, expert views & US Department of Energy Professor A.M El-Sharkawi of the University of Washington, USA Sahand Naftiran Company US Department of Energy US Department of Energy and Erogmagx.com US Department of Energy

Yu et al. (2008b)

US Department of Energy

January, 1991 to July, 2007 & May, 1987 to July, 2007 January 1, 1986 to September 30, 2006

Alexandridis and Livanis (2008) Yu et al. (2008a)

US Department of Energy US Department of Energy

January, 1986 to October, 2007 January 1, 2000 to March 31, 2008

He et al. (2009) Zhang, Wu, and Zhang (2010) Phichhang, and Wang (2011) Mehrara et al. (2011)

US Department of Energy US Department of Energy US Department of Energy Not disclosed

September, 1996 to August, 2007 January 2, 1991 to December 31, 2009 1997 to 2010 January 1, 2002 to July 13, 2009

Pang et al. (2011) Haidar and Wolff (2011) Khashman and Nwulu (2011a)

US Department of Energy US Department of Energy US Department of Energy

January, 1992 to August, 2006 January 2, 1986 to March 2, 2010 January, 03 1986 to December 25, 2009

Khashman and Nwulu (2011b) Khazem (2007)

US Department of Energy US Department of Energy, New York Mercantile Exchange Group and US Department of Labor US Department of Energy US Department of Energy US Department of Energy US Department of Energy and online news US Department of Energy, Reuters and IFS US Department of Energy US Department of Energy Not disclosed

January, 03 1986 to December 25, 2009 January 2, 1996—January 20, 2006

FFNN and adaptive linear neural network FFNN RBFNN, support vector machine, and back-propagation FFNN FFNN Fuzzy time series FFNN Group method of data handling neural networks FFNN FFNN Support vector machine and back-propagation FFNN Support vector machine FFNN

1985 to 2007 January, 1988 to March, 2010 1949 to 2010 January, 1970 to October, 2004

FFNN and fuzzy regression model Back-propagation FFNN FFNN Knowledge based systems

1970 to 2005

FFNN

January, 1970 to December, 2002 January, 1983 to December, 2006 January 5, 2004 to April 30th, 2007

FFNN, expert systems FFNN and GA Fuzzy logic and FFNN

Abdel-Aal (2008) Gholamian et al. (2005) Qunli et al. (2007) Kaboudan (2001)

Azadeh et al. (2012) Jammazi and Aloui (2012) Sotoudeh and Farshad (2012) Yu et al. (2005) Xu et al. (2007) Wang et al. (2004) Reza & Ahmadi (2007) Ghaffari and Zare (2009)

(Continued).

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Table 1. (Continued).

Reference Fan et al. (2008)

Source of Data US Department of Energy

Pan, Haidar, and Kulkarni (2009) Yu et al. (2008c)

US Department of Energy Not disclosed

He et al. (2009)

Global Financial Data

Raudys (2005)

West Texas Intermediate, Natural Gas—Henry, Fuel oil No. 2 (NY), Gasoline, Unld Reg. Non—oxy (NY) and American Stock Exchange FFNN www.barchart.com US Department of Energy and Annual Statistical Bulletin on OPEC’s website

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Malliaris and Malliaris (2008) Aladwani and Iledare (2005)

Period March 20, 1987 to July 26, 2005 and January 2, 1986 to July 26, 2005 January, 1996 to August, 2007 January 1, 2000 to December 31, 2007 April 4, 1983 to June 30, 2006, May 20, 1987 to June 30, 2006 & January 20, 1997 to January 30, 2005 November 1, 1993 to January 12, 2005

December, 1997 to November, 2002 January 19,980 to December 1983

in the literature considered for this review. Phichhang, and Wang (2011) argue that a large amount of data provides a better generalization capability for a neural network forecasting model than a small amount of historical data. Zhang and Zhou (2004) noted that long training periods and very large samples of data are ideal for building robust models in data mining. The computational intelligence technique that attracts a substantial amount of attention from researchers in this domain is neural networks, despite their susceptibility to limitations (refer to Section 3), as indicated in Column 4 of Table 1. These limitations are responsible for undermining the robustness of the neural network (Zhang & Zhou, 2004). Such attention is attributed to the following capabilities of neural networks; to tolerate some missing and erroneous values; any association can be modeled; and new cases can automatically be accommodated by updating the learning (Niculescu, 2003).

These characteristics make neural networks ideal for tackling the nonlinear, non-stationary, chaotic, volatile, and complex nature of crude oil price time series. Least-squares support vector machines are applied to forecast crude oil price, because it is easier to train and has a good modeling performance (Bao et al., 2007). RBFNNs are used to forecast crude oil price to address the nonlinear, non-stationary, and volatile nature of crude oil price (Qunli, Ge, & Xiaodong, 2009). Fernández (2006) applied a support vector machine to crude oil price forecasting due its user friendliness. An evolutionary immune clustering algorithm was utilized to optimize the centers of an RBFNN to build a crude oil forecasting model, to avoid trial and error, and to avoid searching within a limited space (a space that could not lead to the optimal model) (Ma, 2009).

However, the width, hidden-layer neurons, and connection weights are also significant in the RBFNN design and must be optimized with the immune clustering algorithms, because they lack the ideal framework for choosing the best values.

Qi and Zhang (2009) use a cluster support vector machine to forecast crude oil price to reduce the computational complexity of the conventional support vector machine. Alizadeh and Mafinezhad (2010) applied a GRNN due to the complexity of the crude oil price. Kaboudan (2001) apply GPs and FFNNs to crude oil price forecasting, because statistical methods are not sufficient to address the sophisticated complexity of crude oil price dynamics (Kaboudan, 2001). GP and FFNN were applied as IISs and not as HISs, which makes them vulnerable to the limitations of IISs (see Section 3). Aladwani and Iledare (2005) apply GRNNs to predicting oil price, because of its capability of

Technique GA FFNN Back-propagation FFNN, support vector machine and RBFNN FFNN

FFNN GRNN

performing universal approximation for smooth functions. Yu, Wang, and Lai (2008b) forecasted crude oil price with several FFNNs that were recombined with an adaptive linear neural network to produce an ensemble forecast of the price, because the ensemble result is better than the IIS result. Both of the techniques were neural networks and therefore suffer from the limitations that are associated with that technique. A RBFNN, support vector machine, and back-propagation FFNN were integrated to build a HIS for crude oil price forecasting to overcome the limitations attributed to IISs (Yu, Wang, & Lai, 2008a; Yu, Wang, Wen, & Lai, 2008c). However, parameter selection is based on trial and error, which is time consuming and lacks justification. Mehrara, Moeini, and Ahrari (2011) used the group method of data handling neural networks for crude oil price forecasting, because it extracts knowledge directly from sample data and the knowledge is required to improve the forecasting accuracy. Khashman and Nwulu (2011a, 2011b) apply support vector machines, because of their immunity to local minima.

However, the group method of data handling for neural networks, a RBFNN, a GRNN, a cluster support vector machine, a least-squares support vector machine, and a support vector machine were applied as an IIS. Adding recurrent structure into an FFNN improves the forecasting accuracy, which makes this approach suitable for detecting temporal patterns; it has the potential of representing associated computational structure in an efficient manner.

Therefore, RNNs have been applied to forecasting crude oil price (Quek et al., 2008). The network structure becomes more complex, which further complicates the selection of optimal parameters; the computation of the error gradient in an RNN also becomes complicated, because more attractors are present in the state space (Blanco, Delgado, & Pegalajar, 2001). Fuzzy regression is integrated into neural networks during the modeling process in (Azadeh et al., 2012), to cope with the noise and small number of datasets that are considered in the task of forecasting crude oil price. Fuzzy rules are integrated with a neural network in (Gholamian, Ghomi, & Ghazanfari, 2005) to guide decision makers in selecting a non-inferior solution by providing an explanation on the reasons for reaching conclusions. Fuzzy logic and neural networks are hybridized in several ways in the literature, but the most widely used approaches in the domain of crude oil price prediction are fuzzy neural networks and adaptive network-based fuzzy inference systems. For example, Azadeh et al. (2012); Liu et al. (2007); Rast (2001) apply fuzzy neural networks by adding knowledge that

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Figure 6. Crude Oil Price Forecasting Publications by Year.

is processed by fuzzy rules before training the neural network model to forecast crude oil price. In the research conducted in (Haidar, Kulkarni, & Pan, 2008; Khan, 1999; Reza & Ahmadi, 2007), an adaptive network-based fuzzy inference system was used to forecast crude oil price, because it combines the power of neural networks and the capabilities of fuzzy inference systems for using human knowledge to make conclusions and decisions (Yu, Wang, & Lai, 2005). Although its performance suffers as the number of inputs and fuzzy rules increase, a systematic framework for determining optimal fuzzy rules is lacking (Malek, Ebadzadeh, & Rahmati, 2012). In addition, its learning algorithms are back propagation or hybrid learning (Ghaffari & Zare, 2009). In this review, only the study in (Reza & Ahmadi, 2007) was found in the literature to apply genetic algorithms for selecting hidden layer neurons, activation functions, and the number of layers and connections. The neural network was trained with the Levenberg-Marquardt gradient descent algorithm to build a crude oil forecasting model.

However, the training algorithms could possibly fall into local minima and over-fit the training data. However, neural networks and GAs are considered the most reliable and promising computational intelligence techniques (Woll & Cooper, 1997), and neural networks are referenced as the most powerful techniques ever established (Ma & Wu, 2010). Five techniques, namely, neural networks, GAs, statistical inference, rule induction, and data visualization, were compared using the following criteria: Optimization capability, computation

complexity, flexibility, interpretability, scalability, ease of problem encoding, autonomy, and accessibility. The criteria were measured based on a five-point scale (very high, high, medium, low, and very low). Neural networks and GAs were found to be the most suitable techniques for extracting knowledge from historical data (Zhang & Zhou, 2004). An opportunity for neural network optimization is provided through GAs by utilizing their strengths and eliminating their limitations (Shapiro, 2002). Experimental evidence in the literature suggests that the optimization of neural networks by GAs converges to an optimum solution (Huang, Chen, Chen, & Chang, 2009) in less computational complexity than for conventional neural networks (Abbas & Aqel, 2003). Thus, optimizing neural networks using GAs is ideal because the limitations attributed to neural network design will be eliminated and the combined approach will thereby become more effective than using neural networks alone. Figure 6 illustrates that the largest number of published papers was recorded in 2008, with 11 publications, whereas the smallest number was recorded in 2004, with only one publication. This trend typically exhibits an increasing number of publications; of the 59 available for this review, 45 of them were published between 2008 and 2012.

Publications in 2002, 2003, and the second quarter of 2013 are scarce. According to He et al. (2009), crude oil price forecasting did not receive adequate attention when compared to the price of other assets despite its global importance. Table 2 indicates that studies that applied HISs to forecasting crude oil price and that compared their results with IISs found that HISs outperform IISs in all of the studies.

Thus, HISs demonstrates superiority over IISs in terms of crude oil price forecasting. Very few references were found in the literature that considered both demand/supply and unexpected events in building the models. These few studies that consider both demand/supply and unexpected events employed expert systems to manage the effects of unexpected events so that all of the factors that are responsible for oil price volatility are considered during the modeling process. Yu et al.

(2005) propose knowledge-based forecasting systems, integrating text mining and rough sets. Text mining is responsible for searching the Internet and internal file systems to collect both regular factors and uncertainty events that influence crude oil price, create metadata repositories, and then generate a pattern

Table 2. Summary of Published Papers by Strengths.

References Wang et al. (2004), Shouyang et al. (2005), Yu et al. (2005), Malik and Nasereddin (2006), Fernández (2006), Bao et al. (2007), Liu et al. (2007), Fan et al.

(2008), Abdel-Aal (2008), Zimberg (2008), Yu et al. (2008a, 2008b, 2008c), Ma (2009), Panella et al. (2011), Mehrara et al. (2011), Jammazi and Aloui (2012).

Kaboudan (2001), Wang et al. (2004), Aladwani and Iledare (2005), Yu et al.

(2005), Shouyang et al. (2005), Malik and Nasereddin (2006), Moshiri and Foroutan (2006), Xie et al. (2006), Liu et al. (2007), Bao et al. (2007), Khazem (2007), Shambora and Rossiter (2007), Lai et al. (2007), Fan et al. (2008), Quek et al. (2008), Malliaris and Malliaris (2008), Yu et al. (2008a, 2008c), Kulkar and Haidar (2009), He et al. (2009), Qi and Zhang (2009), Ma (2009), Qunli et al. (2009), Wang and Yang (2010), Torban (2010), Phichhang, and Wang (2011), Pang et al. (2011), Panella et al. (2011), Chen and Qu (2011), Mehrara et al. (2011), Haidar and Wolff (2011), Khashman and Nwulu (2011a), Jammazi and Aloui (2012).

Gholamian et al. (2005), Yu et al. (2005), Rast (1997), Reza and Ahmadi (2007), Xu et al. (2007), Alexandridis and Livanis (2008), Mingming et al. (2009), Jinliang et al. (2009), Mehdi (2009), Ghaffari and Zare (2009), Mingming and Jinliang (2012), Wang et al. (2012).

Kaboudan (2001), Shouyang et al. (2005), Raudys (2005), Yu et al. (2005), Khazem (2007), Xu et al. (2007), Reza and Ahmadi (2007), Quek et al. (2008), Malliaris and Malliaris (2008), Alexandridis and Livanis (2008), Alizadeh and Mafinezhad (2010), Abdullah and Zeng (2010), Pang et al. (2011).

Strength The authors demonstrated that HISs performs better than IISs.

The authors established that the techniques proposed in their study perform better than the methods chosen for comparison purposes.

A HIS is used for projection of crude oil prices. It was found that HIS is better than the IIS Performed attributes selection

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and rules. Rough sets further refine the pattern and rules generated by text mining and are used to forecast crude oil price.

Sotoudeh and Farshad (2012) stated that forming market information is a very difficult job, because human experts do not truly understand the mechanisms that determine the market.

In contrast, expert systems perform well with complete knowledge and perform poorly with omitted or incomplete information. According to Shouyang et al. (2005), unexpected events are difficult to collect and their impact is not easy to quantify and approximate. Therefore, knowledge-based systems cannot perform well in handling the effect of uncertainty events on crude oil price. In contrast, in view of the limitations of knowledge-based systems and the capabilities of neural networks, neural networks might be the most appropriate technique for handling the impact of unexpected events on crude oil price.

Each study establishes that all of the techniques proposed in the studies have better crude oil price prediction accuracy than the methods (see Table 3) that were chosen for comparison purposes. On the other hand, convergence speed were not reported in the studies. There is only one exception in a study conducted by Abdullah and Zeng (2010), which found that the performance of the proposed model was inferior to the compared methods.

Some studies applied HISs to oil price forecasting; this approach is more effective, but does not compare the effectiveness of the HIS to other methods for evaluation, as indicated in Table 4. Tables 2 and 4 show that very few of the studies in the literature actually conducted dimension reduction. Dimension reduction increases the prediction accuracy and minimizes the computational cost and complexity, as argued in (Zhang, Zhang, & Yang, 2003). The literature that compared their proposal with other chosen techniques did not proceed further and check for statistically significant differences between the forecasting accuracies of the two compared techniques, except in the research conducted in (Azadeh et al., 2012), where the authors took the additional step of measuring the statistical significance of the their results. Furthermore, other studies

did not compare their results with other techniques for performance evaluation. In previous years, the attention of the machine learning and data mining research community has been drawn to the need for validating their results statistically. This trend can be attributed to the growing interest in this research area, the development of real-life applications of these research approaches, and the performance comparisons made among existing, modified, and newly developed algorithms (Demšar, 2006). Only Khazem & Khazem (2007) investigated the relationship among independent variables. All of the other studies did not investigate the extent of the relationship among the independent variables, as stated in Hair, Black, Babin, Anderson, and Tatham (2006), and successful prediction requires a set of independent variables to form a correlation relationship.

Comprehensive and substantial effort is typically made for data cleaning, and pre-processing deserves more attention in the domain of crude oil price forecasting. According to Zhang et al. (2003), it is estimated that approximately 80% of the data mining process is devoted to data cleaning and pre-processing.

Zhang et al. (2003) noted that quality data mining results are obtained from quality data; similarly, poor-quality data typically yields poor results regardless of the intelligence of the algorithms being applied to the task (e.g., forecasting). The model must be simplified, because it is not feasible to consider all of the variables that are involved in the actual problem. Only the variables that have a major impact on the forecasting are considered for the task (Zimberg, 2008). Quek et al. (2008) argued that the inclusion of irrelevant inputs decreases the prediction accuracy and the generalization ability and increases the computational complexity.

4.1. Frequency of Data Collection The frequency of the data collection for several studies is reported in Table 5. In the published papers, data was collected on several different frequencies depending on the research

Table 3. Overview of Published Papers by Compared Methods.

Reference Wang and Yang (2010) Shouyang et al. (2005), Xie et al. (2006) Kulkar and Haidar (2009) Kaboudan (2001), Quek et al. (2008), Malik and Nasereddin (2006), Mehrara et al. (2011), Khashman and Nwulu (2011b) Ma (2009) Bao et al. (2007) Fan et al. (2008) Moshiri and Foroutan (2006), Haidar and Wolff (2011) Torban (2010) Malliaris and Malliaris (2008) Yu et al. (2008a) He et al. (2009) Jammazi and Aloui (2012) Kulkar and Haidar (2009) Chen and Qu (2011) Qunli et al. (2009) Pang et al. (2011) Shambora and Rossiter (2007) Phichhang, and Wang (2011) Yu et al. (2005) Liu et al. (2007) Khazem (2007), Aladwani and Iledare (2005) Abdullah and Zeng (2010) Panella et al. (2011) Lai et al. (2007) Azadeh et al. (2012) Fernández (2006) Alexandridis and Livanis (2008)

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Method/s compared with for evaluation purposes Econometrics model NN and ARIMA SVM NN RBFNN Wavelet transform and L-SVMleast-squares support vector machine Pattern modeling and recognition system and RNN ARIMA and GARCH ARIMA and Structure Vector Error MLR and simple model RBFNN, SVM, and NN Wavelet decomposition value at risk, ARIMA, and GARCH Back-propagation NN, STEO, and WTI future projection prices RNN Polybasic linear regression Wavelet transform Linear relative inventory and nonlinear relative inventory Buy and hold, technical analysis, and naïve RW GARCH Linear regression model, ARIMA, and back-propagation NN RBFNN, Markov chain, and wavelet analysis Regression analysis Hybrid of text mining, an econometrics model, and a back-propagation NN RBFNN Autoregressive Moving Average and GARCH BR, GDX, LM, B, and BFG ARIMA-ANN and ARIMA-SVM NESWM, STEO, and backpropagation NN

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Table 4. Summary of Published Papers by Weaknesses.

References Rast (2001), Pan et al. (2009), Raudys (2005), Gholamian et al. (2005), Reza and Ahmadi (2007), Xu et al. (2007), Haidar et al. (2008), Abdel-Aal (2008), Alexandridis and Livanis (2008), Mehdi (2009), Ghaffari and Zare (2009), Mingming et al. (2009), Lackes et al. (2009), Jinliang et al. (2009), Alizadeh and Mafinezhad (2010), Zhang et al. (2010), Panella et al. (2011), Khashman and Nwulu (2011a), Movagharnejad et al. (2011), Mingming and Jinliang (2012), Wang et al. (2012), Sotoudeh and Farshad (2012).

Kaboudan (2001), Aladwani and Iledare (2005), Raudys (2005), Xie et al.

(2006), Moshiri and Foroutan (2006), Shambora and Rossiter (2007), Khazem (2007), Haidar et al. (2008), Malliaris and Malliaris (2008), Quek et al. (2008), Kulkar and Haidar (2009), Qi and Zhang (2009), Pan et al. (2009), Ma (2009), Lackes et al. (2009), Torban (2010), Movagharnejad et al. (2011), Alizadeh and Mafinezhad (2010), Chen and Qu (2011), Qunli et al. (2009), Zhang et al. (2010), Wang and Yang (2010), Haidar and Wolff (2011), Khashman and Nwulu (2011a, 2011b), Sotoudeh and Farshad (2012).

Rast (2001), Wang et al. (2004), Aladwani and Iledare (2005), Shouyang et al.

(2005), Gholamian et al. (2005), Malik and Nasereddin (2006), Fernández (2006), Moshiri and Foroutan (2006), Xie et al. (2006), Shambora and Rossiter (2007), Liu et al. (2007), Bao et al. (2007), Lai et al. (2007), Fan et al. (2008), Zimberg (2008), Abdel-Aal (2008), Haidar et al. (2008), Yu et al. (2008a, 2008b, 2008c), Kulkar and Haidar (2009), He et al. (2009), Qi and Zhang (2009), Qunli et al. (2009), Jinliang et al. (2009), Ghaffari and Zare (2009), Mingming et al. (2009), Pan et al. (2009), Ma (2009), Lackes et al. (2009), Mehdi (2009), Torban (2010), Wang and Yang (2010), Zhang et al. (2010), Movagharnejad et al. (2011), Chen and Qu (2011), Phichhang, and Wang (2011), Panella et al. (2011), Mehrara et al. (2011), Haidar and Wolff (2011), Khashman and Nwulu (2011a, 2011b), Sotoudeh and Farshad (2012), Jammazi and Aloui (2012), Mingming and Jinliang (2012), Wang et al. (2012).

Weakness The effectiveness of the proposed method (refer to section 3.4) was not compared to the performance of another method, for evaluation.

IISs are used to built a projection model, whereas HISs are more effective.

Attribute selection is not present in these studies.

Table 5. Overview of Published Papers by the Frequency of the Data Collection.

Reference Rast (2001), Raudys (2005), Gholamian et al. (2005), Moshiri and Foroutan (2006), Fernández (2006), Khazem (2007), Shambora and Rossiter (2007), Lai et al. (2007), Yu et al. (2008b), Fan et al. (2008), Malliaris and Malliaris (2008), Quek et al. (2008), Haidar et al. (2008), Zimberg (2008), Yu et al.

(2008a, 2008c), Lackes et al. (2009), Ma (2009), Mingming et al. (2009), Qunli et al. (2009), Kulkar and Haidar (2009), He et al. (2009), Qi and Zhang (2009), Ghaffari and Zare (2009), Pan et al. (2009), Wang and Yang (2010), Panella et al. (2011), Haidar and Wolff (2011), Mehrara et al. (2011), Mingming and Jinliang (2012), Wang et al. (2012).

Kulkar and Haidar (2009), Phichhang, and Wang (2011), Khashman and Nwulu (2011a, 2011b) Kaboudan (2001), Wang et al. (2004), Aladwani and Iledare (2005), Yu et al.

(2005), Shouyang et al. (2005), Xie et al. (2006), Bao et al. (2007), Reza and Ahmadi (2007), Abdel-Aal RE (2008), Alexandridis and Livanis (2008), Zhang et al. (2010), Alizadeh and Mafinezhad (2010), Abdullah and Zeng (2010), Movagharnejad et al. (2011), Pang et al. (2011), Jammazi and Aloui (2012).

Malik and Nasereddin (2006), Torban (2010) Xu et al. (2007), Azadeh et al. (2012), Sotoudeh and Farshad (2012) Liu et al. (2007), Jinliang et al. (2009), Chen and Qu (2011)

objective. A large number of researchers collected data on a daily and monthly frequency during the modeling process.

Very few extracted their data on a weekly, quarterly, or annual basis, whereas others do not disclose their data frequency.

Weekends and unexpected events cause the oil market to be halted, which creates inconsistences and missing points in the daily data. In its place, weekly or monthly data should be used to avoid the missing points (Ma, 2009; Mirmirani & Li, 2004).

The use of monthly data restricts the prediction horizon to monthly intervals and restricts the amount of training and testing data significantly (Ghaffari & Zare, 2009). Additionally, quarterly and annual data avoid missing points in the data, reduce training and testing data more significantly, and restrict the forecast horizon too quarterly and annually, respectively.

Such variables as oil supply, demand inventory, and GDP are not available on a daily frequency, which further complicates the forecasting of crude oil price (Kulkarni & Haidar, 2009).

Inventory data of OECDs are available only on a monthly

Frequency Daily

Weekly Monthly

Quarterly Annually Not disclosed

interval (Lai, He, & Yen, 2007). Variables such as demand and supply are also not available at a daily frequency (Lackes, Börgermann, & Dirkmorfeld, 2009). To reconcile the contradictions on the issue of the frequency of the data collection, the suggested criteria for selecting relevant independent variables for crude oil price forecasting can be considered when collecting data; the criteria include data availability (Khashman & Nwulu, 2011b; Sotoudeh & Farshad, 2012), correlation between dependent and independent variables (Khashman & Nwulu, 2011b), retrieval of the data on a timely basis, consistent availability of the data, and justification of the variable influence on crude oil price (Khashman & Nwulu, 2011b).

5. Conclusions and Future work This paper presents an extensive review of the research conducted on the application of computational intelligence techniques in solving problems of crude oil price

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forecasting. Experimental data were mostly extracted from the US Department of Energy, and the most patronized oil markets are West Texas Intermediate and Brent, likely because of their influence on the global oil markets; they serve as the reference benchmark for trading crude oil in several parts of the world.

Daily and monthly forecasting models received much attention from researchers. This attention might be attributed to the fact that a large amount of data is required to build robust and effective intelligent models or because some of the significant data required for the forecasting are available only at a monthly frequency.

Conventional methods, such as Econometrics, statistics, and mathematics, which were relegated due to their limitations in solving nonlinear, complex, and dynamic problems, are still relevant in the domain of crude oil price forecasting, because of their robustness in modeling the linear constituents of crude oil data and their ability to remove noise in historical data during data pre-processing, which is a step required to improve the forecasting accuracy. Additionally, the conventional methods are used as a benchmark for evaluating the performance of computational intelligence techniques. The integration of wavelet analysis and computational intelligence techniques is attracting unprecedented interest in crude oil price forecasting due to the ability of wavelet analysis to project data into the time scale domain, to be used as an activation function in the hidden layer neurons of a neural network to speed up convergence, and to conduct multi-scale analysis. The statistical evaluation of results is limited in the area of crude oil price forecasting and is required for assessing the proposed forecasting model for real-life applications.

Despite their limitations, neural networks optimized with GAs (Nissinen, Koivo, & Koivisto, 1999) should further be researched to explore the full potential of the techniques in forecasting crude oil price, because of their superiority over other computational intelligence techniques in solving complex, nonlinear, and dynamic problems. Notwithstanding neural networks optimized with GAs, additional computational intelligence techniques, such as Bee algorithms, fruit fly optimization algorithms, fire fly, artificial Bee colony, ant colony, Tabu search, simulated annealing, swarm particle optimization, imperialist competitive algorithm, cuckoo search algorithm, chicken swarm optimization, monarch butterfly, differential evolution and gravitational search algorithms, can further be explored, because this review has clearly demonstrated that these techniques are not explored in this domain despite their promising performance and wide acceptance. Also, flower pollination algorithm can be explored, because it shows improvement over many nature inspired algorithms (Chiroma et al., 2015).

This review can help researchers to quickly identify areas that require further advancement in order to propose novel approaches to the forecasting of crude oil price. Decision makers, government, and business planners require accurate forecasting of crude oil price due to its global significance. Energy demand and supply projection can effectively be tackled with accurate forecasting of crude oil price (Kaboudan, 2001), which can create stability in the oil market (Malliaris & Malliaris, 2008). As such, carbon dioxide emissions (Jiang, Li, Zhang, Sha, & Ji, 2015) can effectively be monitored. In this manner, we can improve economic activities across the globe and reduce or eliminate suffering in communities. We are now working on a soft computing approach that can have the ability to simultaneously predict West Texas Intermediate, Brent and Dubai

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crude oil price benchmarks unlike previous methods that only concentrated on one benchmark.

Disclosure statement No potential conflict of interest was reported by the authors.

Notes on contributors Haruna Chiroma received a B. Tech. and an M.Sc in Computer Science from Abubakar Tafawa Balewa University and Bayero University Kano, respectively.

Chiroma is currently a PhD candidate waiting for VIVA in the Artificial Intelligence Department of the University of Malaya and has published over 80 articles relevant to his research interest. He is also an invited reviewer to Applied Energy (Elsevier), Engineering Science and Technology (Elsevier), International Journal of Pervasive Computing and Communications (Emerald), etc.

Sameem Abdul-kareem is an associate professor at the Department of Artificial Intelligence, University of Malaya. She possesses a BSc, MSc, and Ph.D. in Computer Science from the University of Malaya.

She has over 120 publications. She has graduated over 10 Ph.D. students and several Msc students. Haruna Chiroma is currently her Ph.D. student (first author of this paper). She served in several capacities in international conferences and presently the Deputy Dean of research.

Ahmad Shukri Mohd Noor Received a BSc, MSc and a Ph.D. from Coventry University, University College of Science and Technology, Malaysia and University Tun Hussien Onn, respectively. He is a senior lecturer at the UMT and published more than 20 articles relevant to his research interest.

Adamu I.Abubakar is presently an assistant professor at International Islamic University of Malaya, Kuala Lumpur, Malaysia. He has a BSc in Geography, PGD, MSc, and Ph.D. in Computer Science. He has published over 100 articles relevant to his research interest.

Nader Sohrabi Safa is a postdoctoral research fellow in the Centre for Research in Information and Cyber Security, School of ICT, Nelson Mandela Metropolitan University. Nader received his Ph.D. degree in Information Systems in 2014 from Faculty of Computer Science and Information Technology, UM. His research interest is in the domain of human interaction with systems and human aspects of information security. He received his bachelor in 1999 and master’s degree in 2005. He has 15 years of experience in analyzing, designing, and programming different systems with C# and SQL server. He has over 10 publications.

Liyana Shuib received a Ph.D. from University of Malaya, Master of Information Technology from Universiti Kebangsaan Malaysia, and a BCompSc (Hons) from Universiti, Teknologi Malaysia, Skudai, Malaysia. She is a senior lecturer in the Department of Information System, Faculty of Computer Science & Information Technology, University of Malaya, Kuala Lumpur. She graduated Ph.D. student and presently supervising several post graduate students. She has more than 20 papers relevant to her research interest.

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Mukhtar Fatihu Hamza is a lecturer at the Bayero University Kano and currently a Ph.D. scholar at the University of Malaya, Faculty of Engineering.

Hamza received his B.Eng. and M.Eng. from Bayero University Kano. Hamza research interest includes controllers, computational intelligence algorithms, intelligent controllers, materials, etc. He has published over 10 articles relevance to his research interest.

Abdulsalam Ya’u Gital is a lecturer at the Mathematical Science Department of the Abubakar Tafawa Balewa University Bauchi, Nigeria. He received his B.Tech., Msc., and PhD in Computer Science from Abubakar Tafawa Balewa University and University of Technology Malaysia, Malaysia, respectively. His area of research interest includes computer communications with special interest in virtual environments, cloud computing, big data, software engineering, artificial intelligence, Network security, and bibliometrics. He has published over 30 academic articles in international journals, international conferences proceedings and book chapters relevant to his research interest. He is a member of the IEEE and ACM. Tutut Herawan received a B.Ed. degree and an M.Sc. degree in Mathematics from Universitas Ahmad Dahlan and Universitas Gadjah Mada Yogyakarta Indonesia, respectively. He later obtained a Ph.D. in Computer Science from Universiti Tun Hussein Onn Malaysia. He has successfully co-supervised three Ph.D. students and published more than 250 papers relevant to his research area, including data mining and knowledge discovery, decision support in information system, etc.

References Abbas, Y.A., & Aqel, M.M. (2003). Pattern recognition using multilayer neural-genetic algorithm. Neurocomputing, 51, 237–247.

Abdel-Aal, R.E. (2008). Univariate modeling and forecasting of monthly energy demand time series using abductive and neural networks.

Computers & Industrial Engineering, 54, 903–917.

Abraham, A., Corchado, E., & Corchado, J.M. (2009). Hybrid learning machines. Neurocomputing, 72, 2729–2730.

Abdullah, S.N., & Zeng, X. (2010, July). Machine learning approach for crude oil price prediction with Artificial Neural Networks-Quantitative (ANN-Q) model. In Neural Networks (IJCNN), The 2010 International Joint Conference on (pp. 1–8). IEEE. Abramson, B., & Finizza, A. (1991). Using belief networks to forecast oil prices. International Journal of Forecasting, 7, 299–315.

Aladwani, F., & Iledare, W. (2005). Predicting crude oil price trends using artificial neural network modeling approach. international association of energy economics USA web. In Proceedings of the 2005 Conference of the International Association of Energy Economics, Denver. Retrieved May 19, 2013, from http://www.iaee.org/en/conferences/2005-denver. aspx Alexandridis, A., & Livanis, E. (2008). Forecasting crude oil prices using wavelet neural networks. Proceedings of the 5th FSDET, Athens, Greece (Vol. 8). Retrieved May 10, 2013, from http://users.uom.gr/~aalex/files/ Download/FSDET%202008%20.pdf Alizadeh, A., & Mafinezhad, K. (2010, August). Monthly Brent oil price forecasting using artificial neural networks and a crisis index. In Electronics and Information Engineering (ICEIE), 2010 International Conference On (Vol. 2, pp. V2-465). IEEE. Ardalan, H., Eslami, A., & Nariman-Zadeh, N. (2009). Piles shaft capacity from CPT and CPTu data by polynomial neural networks and genetic algorithms. Computers and Geotechnics, 36, 616–625.

Azadeh, A., Moghaddam, M., Khakzad, M., & Ebrahimipour, V. (2012). A flexible neural network-fuzzy mathematical programming algorithm for improvement of oil price estimation and forecasting. Computers & Industrial Engineering, 62, 421–430.

Bahrammirzaee, A., Ghatari, A.R., Ahmadi, P., & Madani, K. (2011).

Hybrid credit ranking intelligent system using expert system and artificial neural networks. Applied Intelligence, 34, 28–46.

Bahrammirzaee, A. (2010). A comparative survey of artificial intelligence applications in finance: Artificial neural networks, expert system and hybrid intelligent systems. Neural Computing and Applications, 19, 1165–1195.

Blanco, A., Delgado, M., & Pegalajar, M.C. (2001). A real-coded genetic algorithm for training recurrent neural networks. Neural Networks, 14, 93–105.

Barone-Adesi, G., Bourgoin, F., & Giannopoulos, K. (1998). Don’t look back. Risk, 11, 100–103.

Bao, Y., Zhang, X., Yu, L., Lai, K.K., & Wang, S. (2007, July). Hybridizing wavelet and least squares support vector machines for crude oil price forecasting. In Proceedings of the 2nd International Workshop on Intelligent Finance (pp. 1–15).

Bunke, H., & Kandel, A. (Eds.). (2000). Neuro-fuzzy pattern recognition (Vol. 41). Singapore: World Scientific.

Chen, G., & Pham, T.T. (2001). Introduction to fuzzy sets, fuzzy logic, and fuzzy control systems. New York, NY: CRC Press.

Cheng, J., & Li, Q.S. (2008). Reliability analysis of structures using artificial neural network based genetic algorithms. Computer Methods in Applied Mechanics and Engineering, 197, 3742–3750.

Chen, X., & Qu, Y. (2011, October). A prediction method of crude oil output based on artificial neural network. In 2011 International Conference on Computational and Information Sciences (pp. 702–704).

IEEE. Chiroma, H., Abdulkareem, S., Abubakar, A., & Mohammed, J.U. (2013).

Computational intelligence techniques with application to crude oil price projection: A literature survey from 2001-2012. Neural Network World, 23, 523–551.

Chiroma, H., Shuib, N.L.M., Muaz, S.A., Abubakar, A.I., Ila, L.B., & Maitama, J.Z. (2015). A review of the applications of bio-inspired flower pollination algorithm. Procedia Computer Science, 62, 435–441.

Cigizoglu, H.K., & Alp, M. (2006). Generalized regression neural network in modelling river sediment yield. Advances in Engineering Software, 37, 63–68.

De Castro, L.N., & Timmis, J. (2002). Artificial immune systems: A new computational intelligence approach. London: Springer Science & Business Media.

Demšar, J. (2006). Statistical comparisons of classifiers over multiple data sets. The Journal of Machine Learning Research, 7, 1–30.

Elman, J.L. (1990). Finding structure in time. Cognitive Science, 14, 179–211.

Enke, D., & Mehdiyev, N. (2013). Stock market prediction using a combination of stepwise regression analysis, differential evolutionbased fuzzy clustering, and a fuzzy inference neural network. Intelligent Automation & Soft Computing, 19, 636–648.

Fernández, V. (2006). Forecasting crude oil and natural gas spot prices by classification methods (No. 229). Retrieved June 4, 2012, from http://www.webmanager.cl/prontus_cea/cea_2006/site/asocfile/ ASOCFILE120061128105820.pdf Fan, Y., Liang, Q., & Wei, Y.M. (2008). A generalized pattern matching approach for multi-step prediction of crude oil price. Energy Economics, 30, 889–904.

Fattouh, B. (2010). The dynamics of crude oil price differentials. Energy Economics, 32, 334–342.

Gabralla, L.A., & Abraham, A. (2013). Computational modeling of crude oil price forecasting: A review of two decades of research. International Journal of Computer Information Systems and Industrial Management Applications, 5, 729–740.

Gao, L., Zhou, C., Gao, H.B., & Shi, Y.R.(2006). Credit scoring model based on neural network with particle swarm optimization. In Advances in Natural Computation (pp. 76–79). Springer Berlin Heidelberg.

Ghaffari, A., & Zare, S. (2009). A novel algorithm for prediction of crude oil price variation based on soft computing. Energy Economics, 31, 531–536.

Gholamian, M.R., Ghomi, S.F., & Ghazanfari, M. (2005). A hybrid systematic design for multiobjective market problems: A case study in crude oil markets. Engineering Applications of Artificial Intelligence, 18, 495–509.

Haidar, I., Kulkarni, S., & Pan, H. (2008, December). Forecasting model for crude oil prices based on artificial neural networks. In Intelligent Sensors, Sensor Networks and Information Processing, 2008. ISSNIP 2008. International Conference on (pp. 103–108). IEEE. Haidar, I., & Wolff, C.R. (2011). Forecasting crude oil price (revisted).

United State association of energy economics web. Retrieved June 10, 2012, from http://www.usaee.org/usaee2011/submissions/

Downloaded by [Nelson Mandela Metropolitan University] at 02:32 11 January 2016

Intelligent Automation and Soft Computing

OnlineProceedings/Forecasting%20Crude%20Oil%20Price%20 (Revisited). pdf Hamilton, J.D. (2011). Historical oil shocks. In Routledge Handbook of Major Events in Economic History, pp. 239–265, Randall E. Parker and Robert Whaples (Eds). New York, NY: Routledge Taylor and Francis Group.

Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E., & Tatham, R.L. (2006).

Multivariate data analysis, Vol. 6. Upper Saddle River, NJ: Pearson Prentice Hall.

He, K., Xie, C., Chen, S., & Lai, K.K. (2009). Estimating VaR in crude oil market: A novel multi-scale non-linear ensemble approach incorporating wavelet analysis and neural network. Neurocomputing, 72, 3428–3438.

He, K., Yu, L., & Lai, K.K. (2012). Crude oil price analysis and forecasting using wavelet decomposed ensemble model. Energy, 46, 564–574.

Huang, C.Y., Chen, L.H., Chen, Y.L., & Chang, F.M. (2009). Evaluating the process of a genetic algorithm to improve the back-propagation network: A Monte Carlo study. Expert Systems with Applications, 36, 1459–1465.

Jammazi, R., & Aloui, C. (2012). Crude oil price forecasting: Experimental evidence from wavelet decomposition and neural network modeling.

Energy Economics, 34, 828–841.

Jiang, Y.Q., Li, T., Zhang, M., Sha, S., & Ji, Y.H. (2015). WSN-based Control System of Co2 Concentration in Greenhouse. Intelligent Automation & Soft Computing, (ahead-of-print), 1–10.

Jin, Y. (2005). A comprehensive survey of fitness approximation in evolutionary computation. Soft Computing, 9, 3–12.

Jinliang, Z., Mingming, T., & Mingxin, T. (2009, June). Based on waveletBoltzman neural network and kernel density estimation model predict international crude oil prices. In Future Computer and Communication, 2009. FCC’09. International Conference on (pp. 150–153). IEEE. Kaboudan, M.A. (2001). Compumetric forecasting of crude oil prices. In Evolutionary Computation, 2001. Proceedings of the 2001 Congress on (Vol. 1, pp. 283–287). IEEE. Khan, E. (1999). Neural fuzzy based intelligent systems and applications.

In C.J. Lakhmi & N.M. Martin (Eds.), Fusion of neural networks fuzzy sets and genetic algorithms: Industrial applications, 331.

Khashman, A., & Nwulu, N. (2011a, January). Intelligent prediction of crude oil price using Support Vector Machines. In Applied Machine Intelligence and Informatics (SAMI), 2011 IEEE 9th International Symposium on (pp. 165–169). IEEE. Khashman, A., & Nwulu, I.N. (2011b). Support vector machine versus back propagation algorithms for oil prediction. Lecture Notes in Computer Science, 530–538.

Khazem, H.A. (2007). Using artificial neural network to forecast the futures price of crude oil. (PhD dissertation). Nova South Eastern University.

Kulkarni, S., & Haidar, I. (2009). Forecasting model for crude oil price using artificial neural networks and commodity futures prices.

International Journal of Computer Scienc & Information security, 2, 81–88.

Lackes, R., Börgermann, C., & Dirkmorfeld, M. (2009). Forecasting the price development of crude oil with artificial neural networks. In Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living Vol. 5518, (pp. 248–255).

Berlin Heidelberg: Springer.

Lai, K.K., He, K., & Yen, J. (2007). Modeling VaR in crude oil market: A multi scale nonlinear ensemble approach incorporating wavelet analysis and ANN. In Computational Science–ICCS 2007 (pp. 554–561).

Springer Berlin Heidelberg.

Li, X., Zhang, Y., Wong, H.S., & Qin, Z. (2009). A hybrid intelligent algorithm for portfolio selection problem with fuzzy returns. Journal of Computational and Applied Mathematics, 233, 264–278.

Liu, J., Bai, Y., & Li, B. (2007, August). A new approach to forecast crude oil price based on fuzzy neural network. In Proceedings of IEEE Computer Society Fourth International Conference on Fuzzy System and Knowledge Discovery art (pp. 273–277). IEEE. Ma, X. (2009, October). Fuel oil price forecasting using symbiotic evolutionary immune clustering neural network. In Intelligent Computation Technology and Automation, 2009. ICICTA’09. Second International Conference on (Vol. 1, pp. 322–325). IEEE. Ma, H., & Wu, Y. (2010, October). A grey forecasting model based on BP neural network for crude oil production and consumption in China.

In Information Processing (ISIP), 2010 Third International Symposium on (pp. 105–109). IEEE.

13

Malek, H., Ebadzadeh, M.M., & Rahmati, M. (2012). Three new fuzzy neural networks learning algorithms based on clustering, training error and genetic algorithm. Applied Intelligence, 37, 280–289.

Malliaris, M.E., & Malliaris, S.G. (2008). Forecasting energy product price.

The European Journal of Finance, 14, 453–468.

Malik, F., & Nasereddin, M. (2006). Forecasting output using oil prices: A cascaded artificial neural network approach. Journal of Economics and Business, 58, 168–180.

Mehrara, M., Moeini, A., & Ahrari, M. (2011). Comparison of two approach of neural network for forecasting of oil futures prices. Asian Journal of Business and Management Sciences, 1, 54–64.

Mingming, T., Jinliang Z., & Mingxin, T. (2009). Effects simulation of international natural gas price on crude oil price based on WBNNK model. In IEEE computer society Six International Conference on Natural Computation (pp. 1643–1648). IEEE. Mingming, T., & Jinliang, Z. (2012). A multiple adaptive wavelet recurrent neural network model to analyze crude oil prices. Journal of Economics and Business, 64, 275–286.

Mirmirani, S., & Li, H.C. (2004). A comparison of VAR and neural networks with genetic algorithm in forecasting price of oil. Advances in Econometrics, 19, 203–223.

Moshiri, S., & Foroutan, F. (2006). Forecasting nonlinear crude oil futures prices. The Energy Journal, 27(4):81–95.

Morana, C. (2001). A semiparametric approach to short-term oil price forecasting. Energy Economics, 23, 325–338.

Movagharnejad, K., Mehdizadeh, B., Banihashemi, M., & Kordkheili, M.S. (2011). Forecasting the differences between various commercial oil prices in the Persian Gulf region by neural network. Energy, 36, 3979–3984.

Negnevitsky, M. (2005). Artificial intelligence: A guide to intelligent systems.

Reading, MA: Addison-Wesley.

Niculescu, S.P. (2003). Artificial neural networks and genetic algorithms in QSAR. Journal of Molecular Structure: THEOCHEM, 622, 71–83.

Nissinen, A.S., Koivo, H.N., & Koivisto, H. (1999). Optimization of neural network topologies using genetic algorithm. Intelligent Automation & Soft Computing, 5, 211–223.

Phichhang, O.H., & Wang, H.S. (2011, July). Applications of neural networks in modeling and forecasting volatility of crude oil markets: Evidences from US and China. In Advanced Materials Research (Vol. 230, pp. 953–957).

Qi, Y.L., & Zhang, W.J. (2009, June). The Improved SVM Method for Forecasting the Fluctuation of International Crude Oil Price. In Electronic Commerce and Business Intelligence, 2009. ECBI 2009.

International Conference on (pp. 269–271). IEEE. Pan, H., Haidar, I., & Kulkarni, S. (2009). Daily prediction of short-term trends of crude oil prices using neural networks exploiting multimarket dynamics. Frontiers of Computer Science in China, 3, 177–191.

Panella, M., Barcellona, F., Santucci, V., & D’Ecclesia, R.L. (2011). Neural networks to model energy commodity price dynamics. In Proceedings of the USAEE/IAEE North American Conference. Retrieved 13 March, 2013, from http://www.usaee.org/usaee2011/submissions/ OnlineProceedings/Panella.pdf.

Pang, Y., Xu, W., Yu, L., Ma, J., Lai, K.K., Wang, S., & Xu, S. (2011).

Forecasting the crude oil spot price by wavelet neural networks using OECD petroleum inventory levels. New Mathematics and Natural Computation, 7, 281–297.

Quek, C., Pasquier, M., & Kumar, N. (2008). A novel recurrent neural network-based prediction system for option trading and hedging.

Applied Intelligence, 29, 138–151.

Qunli, W., Ge, H., & Xiaodong, C. (2009). Crude oil price forecasting with an improved model based on wavelet transform and RBF neural network. In Information Technology and Applications, May 2009.

International Forum on (Vol. 1, pp. 231–234). IEEE. Qunli, W., Ge, H., & Xiaodong, C. (2009, May). Crude oil price forecasting with an improved model based on wavelet transform and RBF neural network. In Information Technology and Applications, 2009. IFITA’09.

International Forum on (Vol. 1, pp. 231–234). IEEE. Rast, M. (2001, July). Fuzzy neural networks for modelling commodity markets. In IFSA World Congress and 20th NAFIPS International Conference, 2001. Joint 9th (Vol. 2, pp. 952–955). IEEE. Rast, M. (1997, September). Application of fuzzy neural networks on financial problems. In Fuzzy Information Processing Society, 1997.

NAFIPS’97., 1997 Annual Meeting of the North American (pp. 347–349).

IEEE.

Downloaded by [Nelson Mandela Metropolitan University] at 02:32 11 January 2016

14

H. Chiroma et al.

Raudys, A. (2005). Accuracy of MLP based data visualization used in oil prices forecasting task. In Image Analysis and Processing–ICIAP 2005 (pp. 761–769). Springer Berlin Heidelberg.

Reza, M.A., & Ahmadi, E.G. (2007). A hybrid artificial intelligence approach to monthly forecasting of crude oil prices time series.

The Proceedings of the 10th International Conference on Engineering Application of Neural Networks, CEUR-WS284 160– 167.

Searson, D. (2005). Non-linear PLS using genetic programming (Doctoral dissertation). School of Chemical Engineering and Advanced Materials University of Newcastle upon Tyne An online version of a thesis submitted to the Faculty of Engineering, University of Newcastle upon Tyne.

Shambora, W.E., & Rossiter, R. (2007). Are there exploitable inefficiencies in the futures market for oil? Energy Economics, 29, 18–27.

Shakeri, M., Alibeigloo, A., & Morowat, A. (2005). Multi-objective optimization of laminated cylindrical panels using a genetic algorithm.

In Proc. of the tenth international conference on civil, structural and environmental engineering computing.

Shapiro, A.F. (2002). The merging of neural networks, fuzzy logic, and genetic algorithms. Insurance: Mathematics and Economics, 31, 115– 131.

Shouyang, W., Lean, Y., & Lai, K.K. (2005). Crude oil price forecasting with TEI@I methodology. Journal of Systems Science and Complexity, 18(2), 145–166.

Sivanandam, S. N., & Deepa, S.N. (2007). Introduction to genetic algorithms.

New York: Springer Science & Business Media.

Suykens, J.A., & Vandewalle, J. (1999). Least squares support vector machine classifiers. Neural Processing Letters, 9, 293–300.

Sotoudeh, M., & Farshad, E. (2012). Application of neural network for forecasting gas price in America. Journal of mathematics and computer Science, 4, 2016–2226.

Torban, F.B. (2010). Models for oil price prediction and forecasting (Doctoral dissertation). San Diego State University.

Verikas, A., Kalsyte, Z., Bacauskiene, M., & Gelzinis, A. (2010). Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: A survey. Soft Computing, 14, 995–1010.

Wang, Y., Chen, C., & Yan, X. (2013). Modified radial basis function neural network integrated with multiple regression analysis and its application in the chemical industry processes. Intelligent Automation & Soft Computing, 19, 469–486.

Wang, J., Pan, H., & Liu, F. (2012). Forecasting crude oil price and stock price by jump stochastic time effective neural network model. Journal of Applied Mathematics. doi:10.1155/2012/646475.

Wang, T., & Yang, J. (2010). Nonlinearity and intraday efficiency tests on energy futures markets. Energy Economics, 32, 496–503.

Wang, S., Yu, L., & Lai, K.K. (2005). A novel hybrid AI system framework for crude oil price forecasting. In Data Mining and Knowledge Management (pp. 233–242). Springer Berlin Heidelberg.

Woll, S.L., & Cooper, D.J. (1997). Pattern-based closed-loop quality control for the injection molding process. Polymer Engineering & Science, 37, 801–812.

Xie, W., Yu, L., Xu, S., & Wang, S. (2006). A new method for crude oil price forecasting based on support vector machines. In Computational Science–ICCS 2006 (pp. 444–451). Springer Berlin Heidelberg.

Xu, W., Wang, J., Zhang, X., Zhang, W., & Wang, S. (2007). A new hybrid approach for analysis of factors affecting crude oil price. In Computational Science–ICCS 2007 (pp. 964–971). Springer Berlin Heidelberg.

Ye, M., Zyren, J., & Shore, J. (2006). Forecasting short-run crude oil price using high-and low-inventory variables. Energy Policy, 34, 2736–2743.

Yu, L., Wang, S., & Lai, K.K. (2005). A rough-set-refined text mining approach for crude oil market tendency forecasting. International Journal of Knowledge and Systems Sciences, 2, 33–46.

Yu, L., Wang, S., & Lai, K.K. (2008a, October). A generalized Intelligentagent-based fuzzy group forecasting model for oil price prediction. In Systems, Man and Cybernetics, 2008. SMC 2008. IEEE International Conference on (pp. 489–493). IEEE. Yu, L., Wang, S., & Lai, K.K. (2008b). Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm. Energy Economics, 30, 2623–2635.

Yu, L., Wang, S., Wen, B., & Lai, K.K. (2008c, June). An AI-agent-based trapezoidal fuzzy ensemble forecasting model for crude oil price prediction. Innovative Computing Information and Control, 2008.

ICICIC’08. 3rd International Conference on (pp. 327–327). IEEE. Zalloi, M.M. (2009). Forecasting momentary price of crude oil and gas fossil energies in the world markets through fuzzy based modeling.

In Proceedings of the 24th World Gas Conference, Argentina, 5-9 October.

Zhang, X., Wu, Q., & Zhang, J. (2010, October). Crude oil price forecasting using fuzzy time series. In Knowledge Acquisition and Modeling (KAM), 2010 3rd International Symposium on (pp. 213–216). IEEE. Zhang, S., Zhang, C., & Yang, Q. (2003). Data preparation for data mining.

Applied Artificial Intelligence, 17, 375–381.

Zhang, D., & Zhou, L. (2004). Discovering golden nuggets: Data mining in financial application. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 34, 513–522.

Zimberg, B. (2008). Crude oil price forecasting with ANFIS. University of Vaasa web. Retrieved from http://lipas. uwasa. fi/~ phelo/ ICIL2008TelAviv/24. pdf.