Machine Learning and ARIMA Model Averaging for Adaptive Public Health Forecasting: Comparative Evaluation and an Ontario COVID-19 Case Study
These findings support selecting forecasting models according to operating conditions rather than relying on a single universally preferred approach, and provide a practical framework for combining complementary statistical and machine-learning forecasts.