Building a Decision Support System for Crude Oil Price Prediction using Bayesian Networks

Nuka Nwiabu, Mirabel Amadi

Abstract


Decision Support Systems are computer based systems that are aimed at assisting decision-makers in taking productive, agile, innovative and reputable decisions. This work presents a Decision Support System using Bayesian Network to predict crude oil price .Bayesian Network technology and its application in predicting crude oil price is presented. Price data obtained from the Central Bank of Nigeria was classed into High and Low cases to denote the upward and downward price movement in which information was revealed. The input data were used in this model to train the network and to validate its generalization ability in other to deliver the best prediction forecast. A linguistic prediction model which utilized the Bayesian Network whose aim was to integrate linguistic information into a quantitative prediction model was established. The results obtained from the linguistic model demonstrate that linguistic information adds value to oil price prediction.


Keywords


Bayesian Networks (BNs); Crude oil price; decision support system (DSS)

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References


V. D. Hemant,J. Power and S. Daewon “Decision Support Systems”, 43(4), 2007.

J.M. Druzdzel and R.R. Flynn, Information Science. Librarianship, Ed. Drake, 2005.

E.Forman and M.A,Selly,Decision by Objectives: How to Convince Others That You Are Right, World Scientific Publishing, River Edge, NJ. 402, 2001.

E. Turban and J.Aronson,Decision support systems and intelligence systems, 6thed. New Jersy: Printice Hall, 2001.

D. Power, "Decision support systems: A historical overview," in Handbook on decision support systems 1: Basic themes, F. Burstein and C. W. Holsapple, Eds., ed Berlin: Springer-Verlog, 2008, p. 126.

K. B. Korb andA. E.Nicholson Bayesian Artificial Intelligence. 2nd ed. Available: doi:10.1201/b10391.2010.

J. Pearl and S. Russell“Bayesian Networks”.2000.

R. Daly Q. Shen S.Aitken Learning Bayesian networks: approaches and issues. Knowl Eng Rev 26: 99–157. 2011. Available: http:// www.journals.cambridge.org/abstract_S0269888910000251.

D.Barber. “Bayesian Reasoning and Machine Learning”. Available: www.cs.ucl.ac.uk/staff/D.Barber/brml. 2011.

M. J. Flores,A. E. Nicholson, A. Brunskill,K. B. Korb,S . Mascaro “Incorporating expert knowledge when learning Bayesian network structure”: a medical case study. Artif Intell Med 53: 181–204. Available: http://www.ncbi.nlm.nih.gov/pubmed/21958683. 2011.

K. Jayasurya, G. Fung, S. Yu,C. Dehing-Oberije,D. Ruysscher D et al. “Comparison of Bayesian network and support vector machine models for two-year survival prediction in lung cancer patients treated with radiotherapy”. Med Phys 37: 1401–1407. 2010. Available: http://link.aip.org/link/MPHYA6/v37/i4/p1401/s1&Agg=doi.

K.Amadeo. “US Economy: News & Issues,” How are Oil Prices Determined? 2012.

Y.Nelson, S.Stoner and G.Gemis (1994) “Results of Delphi VIII Survey of Oil Price Forecasts,” in Energy Report, California, California Energy Commission. 1994/

E. Lotfi and H. Navidi. A Decision Support System for OPECOil Production Level based on Game Theory and ANN. Advances in Computational Mathematics and its Applications (ACMA) 253Vol. 2, No. 1, 2012, ISSN 2167-6356Copyright © World Science Publisher, United States. www.worldsciencepublisher.org

H. Schmidbauer,A. Rösch “OPEC News Announcements: Effects on Oil Price Expectation and Volatility”. doi:10.1016/j.eneco.2012.01.006

A.L.Conraria, and Y. Wen. OPEC´s Oil Exporting Strategy and Macroeconomic (in) stability. Working Papers 2011-013. Availablefrom: Nohttp://hdl.handle.net/1822/11993

K. Rajaand S.K. Srivatsa. A Distributed Data Management in Knowledge Based GroupDecision Support Systems. Journal of AppliedSciences, 6: 27-30. 2006.

H. Chiroma, S. Abdul-Kareem,A. Abubakar, A. M. Zeki and M. J. Usman"Orthogonal Wavelet Support Vector Machine for Predicting Crude Oil Prices," in Proceedings of the First International Conference on Advanced Data and Information Engineering (DaEng-2013), 2015, pp. 193-201.

W.Xie, L.Yu, S.Xu, andS.Wang. "A New Method for Crude Oil Price Forecasting Based on Support Vector Machines," in Computational Science–ICCS 2006, ed: Springer, pp. 444-451.

The Central Bank of Nigeria (CBN), www.cbn.gov.ng [Jan 12th 2017]


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