Comparing the Predictive Accuracy of Traditional Linear (ARIMA) and Nonlinear Recurrent Neural Network (LSTM) Models for Inflation Forecasting in Zimbabwe

Prince Ndaruza and Kin Sibanda
Additional contact information
Prince Ndaruza: PhD Candidate (Data Science), School of Engineering Science and Technology, Department of ICT and Electronics, Chinhoyi University of Technology, Chinhoyi, Zimbabwe*
Kin Sibanda: Professor (Economics), Faculty of Commerce, Department of Economics and Economic History, Rhodes University, Rhodes, South Africa

International Journal of Research and Innovation in Social Science, 2025, vol. 9, issue 10, 5874-5884

Abstract: The study compared the forecasting performance of a univariate ARIMA model (traditional econometric model) and a univariate LSTM model (artificial neural network model) in predicting inflation. The purpose was to determine a more appropriate model for forecasting inflation in Zimbabwe, a country that is beset by episodes of high inflation. The research utilized monthly inflation rates of Zimbabwe spanning the time from November 2019 to November 2022. In forecasting inflation, the univariate ARIMA model and the LSTM model were each used separately. The performance of the models was evaluated by using the root mean square error (RMSE) metric. Univariate LSTM outperformed univariate ARIMA by a big margin, having an RSME of 0.14 compared to 6.7 for univariate ARIMA. The findings confirm the hypothesis that the nonlinearity in inflation data is more accurately explained by LSTM. The study contributes to the growing body of evidence that nonlinear neural network models are superior to traditional linear models in terms of predicting time series data with non-linearity.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.rsisinternational.org/journals/ijriss/ ... -5884-202511_pdf.pdf (application/pdf)
https://www.rsisinternational.org/journals/ijriss/ ... casting-in-zimbabwe/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bcp:journl:v:9:y:2025:i:10:p:5874-5884

Access Statistics for this article

International Journal of Research and Innovation in Social Science is currently edited by Dr. Nidhi Malhan

More articles in International Journal of Research and Innovation in Social Science from International Journal of Research and Innovation in Social Science (IJRISS)
Bibliographic data for series maintained by Dr. Pawan Verma ().