Inflation, defined as the rise in prices over time, plays a critical role in determining a nation’s economic stability. Careful monitoring and control are required since this phenomenon has an impact on the cost of living in any country. Despite its importance, not much attention has been paid to Canadian inflation research. This study aims to address this gap by forecasting Canada’s inflation using a novel data-driven forecast combination approach. Inflation is influenced by several economic factors, which are reflected in consumer spending patterns. By incorporating various external economic factors such as exchange rates, oil prices, the commodity price index, money supply, interest rates, and unemployment rates; this approach seeks to accurately capture the variations in inflation. This study introduces a simple yet effective data-driven forecast combination approach that integrates implemented time series and machine learning models. The proposed approach bypasses traditional forecasting steps and allows forecast weights to be optimized by minimizing the h-step ahead forecast error sum of squares (FESS). The performance of the proposed approach is evaluated through numerical experiments using simulated data and Canadian inflation data from the Federal Reserve Economic Data and the Bank of Canada. The results demonstrate that the proposed approach outperforms traditional time series and machine learning models, offering superior accuracy and reliability in forecasting inflation. Importantly, the proposed model is robust, showing consistent performance in pre- and postCOVID periods.
