Title :
Ensemble of adaptive neuro-fuzzy inference system using particle swarm optimization for prediction of crude oil prices
Author :
Gabralla, Lubna A. ; Wahby, Talaat M. ; Ojha, Varun Kumar ; Abraham, Ajith
Author_Institution :
Fac. of Comput. Sci. & Inf. Technol., Sudan Univ. of Sci. & Technol., Khartoum, Sudan
Abstract :
Oil is the lifeblood of the global economy. Recently, oil prices have witnessed fluctuations and the prediction of oil prices has become a challenge for researchers. The aim of this research is to design a model that is able to predict the prices of crude oil with good accuracy. We used the daily data from 1999 to 2012 with 14 input factors to predict the price of West Texas Intermediate (WTI), which is a well-known benchmark. We propose an ensemble of Adaptive Neuro-Fuzzy Inference System using a Particle Swarm Optimization algorithm for oil price prediction and the empirical results illustrate high performance and accurate results.
Keywords :
adaptive systems; crude oil; fuzzy neural nets; fuzzy reasoning; particle swarm optimisation; pricing; WTI price prediction; West Texas Intermediate price prediction; adaptive neurofuzzy inference system; crude oil price prediction; particle swarm optimization; Accuracy; Adaptation models; Adaptive systems; Fuzzy logic; Neural networks; Particle swarm optimization; Predictive models; Adaptive Neuro-Fuzzy Inference System; Ensemble; Particle Swarm Optimization; fluctuating crude oil prices; prediction;
Conference_Titel :
Hybrid Intelligent Systems (HIS), 2014 14th International Conference on
Print_ISBN :
978-1-4799-7632-4
DOI :
10.1109/HIS.2014.7086187
