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arXiv · 2505.13521

Zero-Shot Forecasting Mortality Rates: A Global Study

Gabor Petnehazi·Laith Al Shaggah·Jozsef Gall·Bernadett Aradi
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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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Gabor Petnehazi, Laith Al Shaggah, Jozsef Gall, Bernadett Aradi. 2025-05-17. Zero-Shot Forecasting Mortality Rates: A Global Study. https://arxiv.org/abs/2505.13521

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