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

Siméon Koffi

MPRA Paper from University Library of Munich, Germany

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.

Keywords: LSTM; ARIMA; HOLT-WINTERS (search for similar items in EconPapers)
JEL-codes: C15 C81 C88 (search for similar items in EconPapers)
Date: 2022-08-01
New Economics Papers: this item is included in nep-big, nep-cmp and nep-for
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