The influence of ARIMA-GARCH parameters in feed forward neural networks prediction |
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Authors: | Mauri Aparecido de Oliveira |
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Affiliation: | (1) University of S?o Paulo, S?o Paulo, Brazil |
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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. |
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Keywords: | |
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