零样本模型可直接预测全球死亡率，但需选对模型并微调。
Zero-Shot Forecasting Mortality Rates: A Global Study
- 用预训练模型直接预测死亡率，无需针对任务微调。
- CHRONOS在短期预测中优于传统方法，但长期表现仍需改进。
- 适合想快速验证死亡率趋势的研究者或政策制定者。
本研究探索了零样本时间序列预测这一新方法在死亡率预测中的潜力，该方法利用预训练的基础模型，无需针对特定任务进行微调。我们评估了两种先进基础模型（TimesFM 和 CHRONOS），以及传统和机器学习方法，在50个国家、111个年龄组的数据上，覆盖5年、10年和20年三种预测时长。结果显示：虽然CHRONOS在短周期预测中表现良好，优于ARIMA和Lee-Carter模型；而TimesFM则持续表现不佳。对CHRONOS在死亡率数据上进行微调后，其长期预测准确性显著提升。一个在死亡率数据上训练的随机森林模型整体表现最佳。研究结果表明，零样本预测具有潜力，但需谨慎选择模型并进行领域适配。
原文摘要 · Abstract (English)
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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