Zero-Shot Forecasting Mortality Rates: A Global Study

TL;DR

This paper investigates zero-shot forecasting of mortality rates using foundation models, comparing their performance to traditional methods across multiple countries and age groups, and highlighting the importance of model selection and fine-tuning.

Contribution

It introduces the application of zero-shot foundation models to mortality forecasting and evaluates their effectiveness against traditional and machine learning methods.

Findings

CHRONOS performs well in short-term forecasts

Fine-tuning improves long-term accuracy of foundation models

Random Forest achieves the best overall performance

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.