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

Content Provider	Springer Nature Link
Author	Oliveira, Mauri Aparecido
Copyright Year	2010
Abstract	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.
Starting Page	687
Ending Page	701
Page Count	15
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	20
Issue Number	5
e-ISSN	14333058
Language	English
Publisher	Springer-Verlag
Publisher Date	2010-06-18
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Artificial neural networks Transfer functions Time series Monte Carlo simulation Levenberg–Marquardt algorithm Simulation and Modeling
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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