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Forecasting with artificial neural network models

Author

Listed:
  • Rech, Gianluigi

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

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.

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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    Citations

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    Cited by:

    1. 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.
    2. Martha A. Misas A. & Enrique López E. & Carlos A. Arango A. & Juan Nicolás Hernández A., 2004. "No-linealidades en la demanda de efectivo en Colombia: las redes neuronales como herramienta de pronóstico," Revista ESPE - Ensayos sobre Política Económica, Banco de la Republica de Colombia, vol. 22(45), pages 10-57, June.
    3. Timo Teräsvirta & Marcelo C. Medeiros & Gianluigi Rech, 2006. "Building neural network models for time series: a statistical approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(1), pages 49-75.
    4. Anders Bredahl Kock & Timo Teräsvirta, 2016. "Forecasting Macroeconomic Variables Using Neural Network Models and Three Automated Model Selection Techniques," Econometric Reviews, Taylor & Francis Journals, vol. 35(8-10), pages 1753-1779, December.
    5. 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.
    6. Martha Misas & Enrique López & Carlos Arango & Juan Nicolás Hernández, 2003. "La Demanda de Efectivo en Colombia: Una Caja Negra a la Luz de las Redes Neuronales," Borradores de Economia 268, Banco de la Republica de Colombia.
    7. repec:bdr:ensayo:v::y:2004:i:45:p:10-57 is not listed on IDEAS
    8. Tea Šestanović & Josip Arnerić, 2021. "Neural network structure identification in inflation forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(1), pages 62-79, January.
    9. 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.

    More about this item

    Keywords

    Neural networks; forecasting; nonlinear time series;
    All these keywords.

    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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