SSE/EFI Working Paper Series in Economics and Finance
Forecasting with artificial neural network models
Abstract: This paper contains a forecasting exercise on 30 time
series, ranging on several fields, from economy to ecology. The statistical
approach to artificial neural networks modelling developed by the author is
compared to linear modelling and to other three well-known neural network
modelling procedures: Information Criterion Pruning (ICP), Cross-Validation
Pruning (CVP) and Bayesian Regularization Pruning (BRP). The findings are
that 1) the linear models outperform the artificial neural network models
and 2) albeit selecting and estimating much more parsimonious models, the
statistical approach stands up well in comparison to other more
sophisticated ANN models.
Keywords: Neural networks; forecasting; nonlinear time series; (follow links to similar papers)
JEL-Codes: C22; C53; (follow links to similar papers)
35 pages, February 11, 2002
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