Abstract
Ináation forecasting is key in achieving the Central Bank mandate of price sta- bility the world over. Di§erent traditional methods were used to forecast ináation with little or no attention given to the area of forecasting the ináation rate in Nigeria using machine learning techniques. Data was sourced from CBN statistical bulletin (2021) on monthly basis. The study found that ridge regression and ArtiÖcial Neural Networks are the best in forecasting ináation in Nigeria when compared with the LASSO, elastic net, and PLS. The study further reveals that the major drivers of headline ináation in Nigeria were food ináation, core ináation, prime lending rate, maximum lending rate, and the inter-bank rate. The study recommends that ridge regression and ArtiÖcial Neural Network machine learning techniques be used in forecasting the ináation rate in Nigeria. Also, recommended is the need for the monetary authorities to focus more on ways to improve food production by improving security.
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