用大模型提升时间序列预测的可靠性，尤其在数据少时效果更明显。
Foundation models for time series forecasting: Application in conformal prediction
- 用时间序列大模型替代传统方法，提升预测准确性。
- 数据少时，大模型生成的预测区间更可靠，误差更小。
- 适合数据稀缺场景，如医疗、金融等小样本应用。
时间序列基础模型（TSFMs）在零样本预测中展现出潜力，尤其在可将大量数据用于校准的分位数预测设置下。本研究对比了TSFMs与经典统计模型和梯度提升模型在分位数预测中的表现。结果表明：当数据量有限时，TSFMs因预测精度更高，能提供更可靠的分位数预测区间；同时，由于更多数据可用于校准，校准过程更稳定。数据越少，优势越显著，因传统模型需大量数据才能有效训练。这些发现凸显了基础模型在时间序列分位数预测中的价值，尤其在数据受限情况下。所有实验代码均已公开。
原文摘要 · Abstract (English)
The zero-shot capabilities of foundation models (FMs) for time series forecasting offer promising potentials in conformal prediction, as most of the available data can be allocated to calibration. This study compares the performance of Time Series Foundation Models (TSFMs) with traditional methods, including statistical models and gradient boosting, within a conformal prediction setting. Our findings highlight two key advantages of TSFMs. First, when the volume of data is limited, TSFMs provide more reliable conformalized prediction intervals than classic models, thanks to their superior predictive accuracy. Second, the calibration process is more stable because more data are used for calibration. Morever, the fewer data available, the more pronounced these benefits become, as classic models require a substantial amount of data for effective training. These results underscore the potential of foundation models in improving conformal prediction reliability in time series applications, particularly in data-constrained cases. All the code to reproduce the experiments is available.
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