Zero-Shot Forecasting Mortality Rates: A Global Study

• URL: http://arxiv.org/abs/2505.13521v1
• Date: Sat, 17 May 2025 13:27:39 GMT
• Title: Zero-Shot Forecasting Mortality Rates: A Global Study
• Authors: Gabor Petnehazi, Laith Al Shaggah, Jozsef Gall, Bernadett Aradi,
• Abstract summary: We evaluate two state-of-the-art foundation models, TimesFM and CHRONOS, alongside traditional and machine learning-based methods.<n>Fine-tuning CHRONOS on mortality data significantly improved long-term accuracy.<n>A Random Forest model, trained on mortality data, achieved the best overall performance.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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