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A Simple Strategy to Prune Neural Networks with an Application to Economic Time Series

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Author Info
J.F. Kaashoek ()
H.K. van Dijk () (FEW-Econometrie en besliskunde)

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Abstract

A major problem in applying neural networks is specifying the size of the network. Even for moderately sized networks the number of parameters may become large compared to the number of data. In this paper network performance is examined while reducing the size of the network through the use of multiple correlation coefficients, principal component analysis of residuals and graphical analysis of network output per hidden layer cell and input layer cell.

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File URL: http://www.eur.nl/WebDOC/doc/econometrie/feweco19990330085659.ps
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Paper provided by Erasmus University Rotterdam, Econometric Institute in its series Econometric Institute Report with number 103.

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Date of creation: 1998
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Handle: RePEc:dgr:eureir:1998103

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Keywords: neural network pruning exchange rates;

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This paper has been announced in the following NEP Reports: References listed on IDEAS
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  1. Schotman, Peter C & van Dijk, Herman K, 1991. "On Bayesian Routes to Unit Roots," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 6(4), pages 387-401, Oct.-Dec.. [Downloadable!] (restricted)
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  1. J.F. Kaashoek & H.K. van Dijk, 1999. "Neural networks analysis of varying trends in real exchange rates," Econometric Institute Report 143, Erasmus University Rotterdam, Econometric Institute. [Downloadable!]
  2. Marcelo C. Medeiros & Carlos E. Pedreira, 2001. "What are the effects of forecasting linear time series with neural networks," Textos para discussão 446, Department of Economics PUC-Rio (Brazil). [Downloadable!]
  3. J.F. Kaashoek & H.K. Van Dijk, 2001. "Neural networks as econometric tool," Econometric Institute Report 213, Erasmus University Rotterdam, Econometric Institute. [Downloadable!]
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This page was last updated on 2009-12-16.

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