La prévision de l’inflation par la méthode des réseaux de neurones : Le cas de la Tunisie.

Authors

  • Inès Abdelkafi Unité de Recherche en Economie du Développement
  • Rochdi Feki Unité de Recherche en Economie du Développement
  • Damien Bazin Université de Nice Sophia Antipolis

Keywords:

Inflation rate, forecasting time series, artificial neural networks

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.

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Published

2024-01-16

How to Cite

Abdelkafi, I., Feki, R., & Bazin, D. (2024). La prévision de l’inflation par la méthode des réseaux de neurones : Le cas de la Tunisie. Ética, economía Y Bienes Comunes, 9(1). Retrieved from https://journal.upaep.mx/index.php/EthicsEconomicsandCommonGoods/article/view/226

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Section

Research articles