无需微调，时间序列大模型可直接预测宏观经济指标。
Generalisation Bounds of Zero-Shot Economic Forecasting using Time Series Foundation Models
- 直接使用预训练时间序列模型，跳过传统建模流程。
- 在数据稀缺和结构突变下仍保持稳定预测与合理置信区间。
- 适合快速部署场景，但剧烈经济冲击时性能会下降。
本研究探究了时间序列基础模型（TSFMs）在宏观经济指标上的零样本预测能力。我们采用三种前沿模型（Chronos、TimeGPT、Moirai），在未进行任何定制化调整的情况下，在单变量条件下对宏观经济指标进行预测，避免了传统计量模型所需的大量训练数据与建模过程。实验基于案例数据集，在数据稀缺及结构突变条件下进行了严格回测。结果表明，经过合理设计的TSFMs能内化复杂的经济动态，适应制度变迁，并提供可靠的不确定性估计，其表现可媲美先进的多变量模型。在经济平稳期，无需微调即可达到甚至超越经典模型；但在快速冲击期，性能显著下降。研究为宏观监测与战略规划中的零样本部署提供了实践指导。
原文摘要 · Abstract (English)
This study investigates zero-shot forecasting capabilities of Time Series Foundation Models (TSFMs) for macroeconomic indicators. We apply TSFMs to forecasting economic indicators under univariate conditions, bypassing the need for train bespoke econometric models using and extensive training datasets. Our experiments were conducted on a case study dataset, without additional customisation. We rigorously back-tested three state-of-the-art TSFMs (Chronos, TimeGPT and Moirai) under data-scarce conditions and structural breaks. Our results demonstrate that appropriately engineered TSFMs can internalise rich economic dynamics, accommodate regime shifts, and deliver well-behaved uncertainty estimates out of the box, while matching state-of-the-art multivariate models on this domain. Our findings suggest that, without any fine-tuning, TSFMs can match or exceed classical models during stable economic conditions. However, they are vulnerable to degradation in performances during periods of rapid shocks. The findings offer guidance to practitioners on when zero-shot deployments are viable for macroeconomic monitoring and strategic planning.
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