Using belief networks to forecast oil prices

Abramson, Bruce; Finizza, Anthony J.

doi:10.1016/0169-2070(91)90004-f

Cited by 74 publications

(31 citation statements)

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“…Research [3] successfully utilised a probabilistic model to predict the oil price. The research was conducted based on a case study about the probabilistic inheritance of Belief Network (BN) models.…”

Section: Research Backgroundmentioning

confidence: 99%

Machine learning approach for crude oil price prediction with Artificial Neural Networks-Quantitative (ANN-Q) model

Abdullah

Zeng

2010

The 2010 International Joint Conference on Neural Networks (IJCNN)

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Section: Research Backgroundmentioning

confidence: 99%

Machine learning approach for crude oil price prediction with Artificial Neural Networks-Quantitative (ANN-Q) model

Abdullah

Zeng

2010

The 2010 International Joint Conference on Neural Networks (IJCNN)

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“…Furthermore, Haas et al (2004), Agnolucci (2009), Cheong (2009) and Jo (2012 devoted effort to analyzing the volatility of the oil prices. Several studies forecasted the oil prices from standard econometric techniques and intelligent computing models such as artificial neural networks and fuzzy expert systems (see for example, Abramson and Finizza, 1991;Pan et al, 2009;and Azadeh et al, 2012).…”

Section: Literature Reviewmentioning

confidence: 99%

Constructing a Composite Leading Indicator for the Global Crude Oil Price

Chong¹,

Puah²,

Mansor³

2018

IBR

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Crude oil, as the most traded commodity in the world, exhibits prices with a clear influence on other commodities in the worldwide market. It also poses implications regarding the economic growth of oil-exporting and oil-importing nations. This study provides an unprecedented method of employing the indicator approach as proposed by the Conference Board, National Bureau of Economic Research, to construct a leading indicator for the global crude oil price. The results reveal that the constructed oil price indicator can predict the cyclical movement of the oil price by moving in advance of 3.5 months on average. This finding could provide better signaling to oil-related nations as well as other commodities that consider crude oil to be a leader in the market.

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“…Forecasting oil prices brought a considerable attention by researchers who provide a proactive knowledge in identifying potential candidate forecasting models for crude oil prices. In their pioneering study, Abramson and Finizza [10][11][12] used Belief Networks (BNs) to forecast crude oil. More recently, they used rather a probabilistic belief network model to address the given question [13].…”

Section: Related Researchmentioning

confidence: 99%

Oil price prediction using ensemble machine learning

Gabralla

Jammazi

Abraham

2013

2013 International Conference on Computing, Electrical and Electronic Engineering (Icceee)

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Abstract-Crude oil price forecasting is a challenging task due to its complex nonlinear and chaotic behavior. During the last couple of decades, both academicians and practitioners devoted proactive knowledge to address this issue. A strand of them has focused on some key factors that may influence the crude oil price prediction accuracy. This paper extends this particular branch of recent works by considering a number of influential features as inputs to test the forecasting performance of daily WTI crude oil price covering the period 4th January 1999 through 10th October 2012. Empirical results indicate that the proposed methods are efficient and warrant further research in this field.

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Using belief networks to forecast oil prices

Cited by 74 publications

References 10 publications

Machine learning approach for crude oil price prediction with Artificial Neural Networks-Quantitative (ANN-Q) model

Machine learning approach for crude oil price prediction with Artificial Neural Networks-Quantitative (ANN-Q) model

Constructing a Composite Leading Indicator for the Global Crude Oil Price

Oil price prediction using ensemble machine learning

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