Zero-Shot Forecasting Mortality Rates: A Global Study

2025/05/17 by Gábor Petneházi, Petnehazi, Gabor, Laith Al Shaggah +5
Environmental Science · Social Sciences · #Applications (stat.AP) #Climate Change and Health Impacts #FOS: Computer and information sciences #FOS: Economics and business #Insurance, Mortality, Demography, Risk Management #Machine Learning (cs.LG) #Risk Management (q-fin.RM)

paper · pdf · doi:10.48550/arxiv.2505.13521

openalex publication_date 2025/05/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• Chronos: Learning the Language of Time Series
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• TimeGPT-1
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• Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
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• Attention Is All You Need
• Long Short-Term Memory
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