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

fYear

2014

Firstpage

141

Lastpage

146

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Hybrid Intelligent Systems (HIS), 2014 14th International Conference on

Print_ISBN

978-1-4799-7632-4

Type

conf

DOI

10.1109/HIS.2014.7086187

Filename

7086187