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Forecasting with artificial neural network models


  • Rech, Gianluigi

    (QA Analysis, ELECTRABEL, Place de l'Universite', 16, LLN, B-1348 Belgium)


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.

Suggested Citation

  • Rech, Gianluigi, 2002. "Forecasting with artificial neural network models," SSE/EFI Working Paper Series in Economics and Finance 491, Stockholm School of Economics.
  • Handle: RePEc:hhs:hastef:0491

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    References listed on IDEAS

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    10. Caputo, Michael R., 1990. "How to do comparative dynamics on the back of an envelope in optimal control theory," Journal of Economic Dynamics and Control, Elsevier, vol. 14(3-4), pages 655-683, October.
    11. Jones-Lee, M W, 1991. "Altruism and the Value of Other People's Safety," Journal of Risk and Uncertainty, Springer, vol. 4(2), pages 213-219, April.
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    Cited by:

    1. Martha Alicia Misasarango & Enrique Antonio Lopezenciso & Carlos Arango & Juan Nicolashernandez, 2004. "No-Linealidades En La Demanada De Efectivo En Colombia: Las Redes Neuronales Como Herramienta De Pronostico," ENSAYOS SOBRE POLÍTICA ECONÓMICA, BANCO DE LA REPÚBLICA - ESPE, vol. 22(45), pages 10-57, June.
    2. Terasvirta, Timo & van Dijk, Dick & Medeiros, Marcelo C., 2005. "Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A re-examination," International Journal of Forecasting, Elsevier, vol. 21(4), pages 755-774.
    3. Anders Bredahl Kock & Timo Teräsvirta, 2010. "Forecasting with nonlinear time series models," CREATES Research Papers 2010-01, Department of Economics and Business Economics, Aarhus University.
    4. Alekseev, K.P.G. & Seixas, J.M., 2009. "A multivariate neural forecasting modeling for air transport – Preprocessed by decomposition: A Brazilian application," Journal of Air Transport Management, Elsevier, vol. 15(5), pages 212-216.
    5. repec:bdr:ensayo:v::y:2004:i:45:p:10-57 is not listed on IDEAS

    More about this item


    Neural networks; forecasting; nonlinear time series;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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