Forecasting with artificial network models - Gianluigi Rech (Stockholm School of Economics, The Economic Research Institute (EFI), 2002)
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: C22; C53; 330; Neural networks; forecasting; nonlinear time series; Neuronale Netze; Schätztheorie; Theorie