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Prévision de l’inflation en Côte D’ivoire : Analyse Comparée des Modèles Arima, Holt-Winters, et Lstm
[Inflation Forecasting in Côte D'Ivoire: A Comparative Analysis of the Arima, Holt-Winters, and Lstm Models]

Author

Listed:
  • Koffi, Siméon

Abstract

This paper attempts to highlight the role of new short-term forecasting methods. It leads to the fact that artificial neural networks (LSTM) are more efficient than classical methods (ARIMA and HOLT-WINTERS) in forecasting the HICP of Côte d'Ivoire. The data are from the “Direction des Prévisions, des Politiques et des Statistiques Economiques (DPPSE)” and cover the period from January 2012 to May 2022. The root mean square error of the long-term memory recurrent neural network (LSTM) is the lowest compared to the other two techniques. Thus, one can assert that the LSTM method improves the prediction by more than 90%, ARIMA by 68%, and Holt-Winters by 61%. These results make machine learning techniques (LSTM) excellent forecasting tools.

Suggested Citation

  • Koffi, Siméon, 2022. "Prévision de l’inflation en Côte D’ivoire : Analyse Comparée des Modèles Arima, Holt-Winters, et Lstm [Inflation Forecasting in Côte D'Ivoire: A Comparative Analysis of the Arima, Holt-Winters, and," MPRA Paper 113961, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:113961
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    File URL: https://mpra.ub.uni-muenchen.de/113961/1/MPRA_paper_113961.pdf
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    References listed on IDEAS

    as
    1. Stock, James H. & Watson, Mark W., 1999. "Forecasting inflation," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 293-335, October.
    2. repec:onb:oenbwp:y::i:73:b:1 is not listed on IDEAS
    3. Nesreen Ahmed & Amir Atiya & Neamat El Gayar & Hisham El-Shishiny, 2010. "An Empirical Comparison of Machine Learning Models for Time Series Forecasting," Econometric Reviews, Taylor & Francis Journals, vol. 29(5-6), pages 594-621.
    4. Blix, Mårten, 1999. "Forecasting Swedish Inflation With a Markov Switching VAR," Working Paper Series 76, Sveriges Riksbank (Central Bank of Sweden).
    5. McAdam, Peter & McNelis, Paul, 2005. "Forecasting inflation with thick models and neural networks," Economic Modelling, Elsevier, vol. 22(5), pages 848-867, September.
    6. Friedrich Fritzer & Gabriel Moser & Johann Scharler, 2002. "Forecasting Austrian HICP and its Components using VAR and ARIMA Models," Working Papers 73, Oesterreichische Nationalbank (Austrian Central Bank).
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    LSTM; ARIMA; HOLT-WINTERS;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software

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