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

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.