The influence of ARIMA-GARCH parameters in feed forward neural networks prediction


Autoria(s): OLIVEIRA, Mauri Aparecido de
Contribuinte(s)

UNIVERSIDADE DE SÃO PAULO

Data(s)

20/10/2012

20/10/2012

2011

Resumo

The objective of this article is to find out the influence of the parameters of the ARIMA-GARCH models in the prediction of artificial neural networks (ANN) of the feed forward type, trained with the Levenberg-Marquardt algorithm, through Monte Carlo simulations. The paper presents a study of the relationship between ANN performance and ARIMA-GARCH model parameters, i.e. the fact that depending on the stationarity and other parameters of the time series, the ANN structure should be selected differently. Neural networks have been widely used to predict time series and their capacity for dealing with non-linearities is a normally outstanding advantage. However, the values of the parameters of the models of generalized autoregressive conditional heteroscedasticity have an influence on ANN prediction performance. The combination of the values of the GARCH parameters with the ARIMA autoregressive terms also implies in ANN performance variation. Combining the parameters of the ARIMA-GARCH models and changing the ANN`s topologies, we used the Theil inequality coefficient to measure the prediction of the feed forward ANN.

Identificador

NEURAL COMPUTING & APPLICATIONS, v.20, n.5, Special Issue, p.687-701, 2011

0941-0643

http://producao.usp.br/handle/BDPI/30794

10.1007/s00521-010-0410-8

http://dx.doi.org/10.1007/s00521-010-0410-8

Idioma(s)

eng

Publicador

SPRINGER

Relação

Neural Computing & Applications

Direitos

restrictedAccess

Copyright SPRINGER

Palavras-Chave #Artificial neural networks #Transfer functions #Time series #Monte Carlo simulation #Levenberg-Marquardt algorithm #TIME-SERIES #MODELS #INFLATION #ALGORITHM #Computer Science, Artificial Intelligence
Tipo

article

original article

publishedVersion