Haruna Chiroma - Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia
Adeleh Asemi Zavareh - Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia
Mohd Sapiyan Baba - Faculty of Computer Science, Gulf University of Science and Technology, Kuwait
Adamu I. Abubakar - Faculty of Information and Communication Technology, International Islamic University Kuala Lumpur, Malaysia.

This research studies the application of hybrid algorithms for predicting the prices of crude oil. Previous studies mainly use expert systems for predicting oil prices basedon the impact of uncertain events, whereas in this paper, neural networks were used due to their ability to automatically handlenew patterns by updating their learning unlike in expert systems. Brent crude oil price data and hybrid intelligent algorithms (time delay neural network, probabilistic neural network, and fuzzy logic) were used to build intelligent decision support systems for predicting crude oil prices. The proposedmodel was able to predict future crude oil prices from August 2013 to July 2014. Future prices can guide decision makers in economic planning and taking effective measures to tackle the negative impact of crude oil price volatility. Energy demand and supply projection can effectively be tackled with accurate forecasts of crude oil prices, which in turn can create stability in the oil market. The future crude oil prices predict by theintelligent decision support systems can be used by both government and international organizations related to crude oil such as organization of petroleum exporting countries (OPEC) for policy formulation in the next one year.

decision support system, time delay neural network, probabilistic neural network, fuzzy logic, crude oil prices