Forecasting The Consumer Price Index in Namibia: A Comparative Analysis of Machine Learning and Statistical Methods
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Date
2025-10-02
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Namibia University of Science and Technology
Abstract
In emerging countries like Namibia, accurate forecasting of the Consumer Price Index (CPI) is important for evidence-based policy development, effective economic planning, and inflation management. However, despite its importance, there has been limited application of advanced forecasting methods within Namibia’s context. The study addressed this gap by investigating and comparing the performance of traditional statistical methods, AutoRegressive Integrated Moving Average (ARIMA), Holt-Winters exponential smoothing with machine learning methods, Long Short-Term Memory (LSTM) recurrent neural network, and Support Vector Regression (SVR) in forecasting Namibia’s CPI using monthly data from 2013 to 2023. The findings revealed that SVR yielded the lowest Root Mean Square Error (RMSE), indicating higher forecast accuracy compared to other models. The study recommends a shift towards machine learning methods, particularly SVR, for CPI forecasting, given its capability to capture nonlinear trends in economic data. The study further recommends enhancing forecasting methods by incorporating relevant economic indicators such as interest rate, GDP growth, unemployment rate, and government expenditure. The study also emphasises the importance of investing in capacity-building for data science and developing real-time data systems to support policy formulation and CPI monitoring. The research contributes to the growing body of evidence that machine learning can enhance CPI forecasting and decision-making processes in developing countries
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Elago , L. (2025). Forecasting The Consumer Price Index in Namibia: A Comparative Analysis of Machine Learning and Statistical Methods [Master’s thesis, Namibia University of Science and Technology].