Zero-Shot Forecasting Mortality Rates: A Global Study
This work addresses mortality rate prediction for global health planning, but it is incremental as it primarily compares existing methods without introducing new techniques.
This study tackled forecasting mortality rates by evaluating zero-shot time series forecasting using pre-trained foundation models, finding that CHRONOS performed competitively in short-term forecasts but fine-tuning or traditional methods like Random Forest yielded better overall accuracy.
This study explores the potential of zero-shot time series forecasting, an innovative approach leveraging pre-trained foundation models, to forecast mortality rates without task-specific fine-tuning. We evaluate two state-of-the-art foundation models, TimesFM and CHRONOS, alongside traditional and machine learning-based methods across three forecasting horizons (5, 10, and 20 years) using data from 50 countries and 111 age groups. In our investigations, zero-shot models showed varying results: while CHRONOS delivered competitive shorter-term forecasts, outperforming traditional methods like ARIMA and the Lee-Carter model, TimesFM consistently underperformed. Fine-tuning CHRONOS on mortality data significantly improved long-term accuracy. A Random Forest model, trained on mortality data, achieved the best overall performance. These findings underscore the potential of zero-shot forecasting while highlighting the need for careful model selection and domain-specific adaptation.