Intelligent decision support systems for oil price forecasting - Chiroma, Haruna; Zavareh, Adeleh Asemi; Baba, Mohd Sapiyan; Ibrahim, Adamu Abubakar; Gital, Abdulsam Ya'u; Zambuk, Fatima Umar (The Interntaional Journal of Information Science and Management, 2015)
This research studies the application of hybrid algorithms for predicting the prices of crude oil. Brent crude oil price data and hybrid intelligent algorithm (time delay neural network, probabilistic neural network, and fuzzy logic) were used to build intelligent decision support systems for predicting crude oil prices. The proposed model 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 the intelligent 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.