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La prévision de l'inflation par la méthode des réseaux de neurones : cas de la Tunisie

Author

Listed:
  • Damien Bazin

    (GREDEG - Groupe de Recherche en Droit, Economie et Gestion - UNS - Université Nice Sophia Antipolis (1965 - 2019) - CNRS - Centre National de la Recherche Scientifique - UniCA - Université Côte d'Azur)

  • Inès Abdelkafi

    (Unité de Recherche en Economie du Développement (URED) - Chercheur indépendant)

  • Rochdi Feki

    (Unité de Recherche en Economie du Développement (URED). - Chercheur indépendant)

Abstract

The neural approach drew the interest of many researchers for time series analysis and forecasting in diverse domains. In this paper, we study the ability of artificial neural networks (ANN) such as "multilayer perceptrons" to predict the Tunisian inflation rate. We try to find a better technical of inflation forecasting by comparing the results obtained using ANN to those provided by linear autoregressive models (AR) and the "naive" forecasting model. The comparison is based on the root-mean-square error (RMSE) criterion and the improvement rate of the latter (measured against the random walk). The results found showed the superiority of the RNA to trace the series evolution and to offer a better performance in terms of predictive power for inflation rate in Tunisia.

Suggested Citation

  • Damien Bazin & Inès Abdelkafi & Rochdi Feki, 2012. "La prévision de l'inflation par la méthode des réseaux de neurones : cas de la Tunisie," Post-Print halshs-00727319, HAL.
    • Handle: RePEc:hal:journl:halshs-00727319
    as

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