CPI Inflation Rate: A Comparative Analysis of Traditional Basic Structural Model (BSM) and Artificial Neural Networks (ANN) Model for Accurate Predictions in South Africa

Precise inflation prediction is crucial for the formulation of monetary policies and economic planning, especially in the case of developing countries like South Africa. This research investigates the predictive capabilities of conventional and machine learning models for forecasting the South African Consumer Price Index (CPI) inflation rate. The monthly values of the CPI inflation rate from January 2008 to October 2025, collected from Statistics South Africa, were considered. The models used were the Basic Structural Model (BSM), the Artificial Neural Network (ANN), and the combined hybrid BSM and ANN approach. The BSM model examines structural variables like trend and seasonality, and the model ANNs focus on nonlinearities. The model performance was assessed using the criteria of RMSE, MAE, and MASE performance measures, in the in-sample forecasting. The empirical analysis shows that the performance of the model ANN outperformed that of the BSM and hybrid BSM–ANN models considerably when compared using the in-sample forecasting, and its results were well-behaved without any features of autocorrelation and heteroscedasticity. The results showed that the combined hybrid BSM–ANN model was superior to the BSM and ANN models, although it may not necessarily outperform the other two, as the out-of-sample forecasts were more stable and reasonable.