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 longer-term 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.
A Study on Crude Oil Prices Modeled by Neurofuzzy Networks / Panella, Massimo; Liparulo, Luca; Barcellona, Francesco; D'Ecclesia, RITA LAURA. - STAMPA. - (2013), pp. 1-7. (Intervento presentato al convegno IEEE International Conference on Fuzzy Systems tenutosi a Hyderabad, India nel 7-10 luglio 2013) [10.1109/fuzz-ieee.2013.6622496].
A Study on Crude Oil Prices Modeled by Neurofuzzy Networks
PANELLA, Massimo;LIPARULO, LUCA;BARCELLONA, FRANCESCO;D'ECCLESIA, RITA LAURA
2013
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 longer-term 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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.