DocumentCode
3384486
Title
A study on crude oil prices modeled by neurofuzzy networks
Author
Panella, Massimo ; Liparulo, Luca ; Barcellona, Francesco ; D´Ecclesia, Rita L.
Author_Institution
Dept. of Inf. Eng., Univ. of Rome La Sapienza, Rome, Italy
fYear
2013
fDate
7-10 July 2013
Firstpage
1
Lastpage
7
Abstract
In the last decade the increasing volatility of petroleum markets has challenged time series analysts to produce highly predictive models. Crude Oil is a major driver of the global economy and its price fluctuations are a key indicator for producers, consumers and investors. With investors following the longerterm upward trend in Energy prices Commodity investments, we believe this will drive an increasing importance for methodologies like neurofuzzy networks for risk quantification, measurement and management. The data used is Crude Oil prices for both Brent and WTI in the 10 year period from 2001 to 2010. We will prove that the neurofuzzy approach based on ANFIS networks compare favorably with respect to other standard and neural models and it is able to achieve useful performances in terms of accurate prediction of prices and their probability distribution.
Keywords
crude oil; forecasting theory; fuzzy neural nets; fuzzy reasoning; pricing; statistical distributions; ANFIS networks; Brent; WTI; adaptive neuro-fuzzy inference system; crude oil prices model; energy prices commodity investments; neurofuzzy networks; petroleum market volatility; price prediction; price probability distribution; time series analysts; Biological system modeling; Data models; Numerical models; Predictive models; Standards; Time series analysis; Training; ANFIS model; Crude Oil price; fuzzy neural network; time series analysis;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Systems (FUZZ), 2013 IEEE International Conference on
Conference_Location
Hyderabad
ISSN
1098-7584
Print_ISBN
978-1-4799-0020-6
Type
conf
DOI
10.1109/FUZZ-IEEE.2013.6622496
Filename
6622496
Link To Document