时间序列大模型更可信：比传统模型更少过自信。
Beyond Accuracy: Are Time Series Foundation Models Well-Calibrated?
- 对比五种时序大模型与两种基线，系统评估校准性。
- 大模型整体校准性更好，长期预测也保持稳定信心。
- 适合需要可靠置信度评估的工业场景应用。
近期时间序列基础模型在多种应用中引发广泛关注。尽管其预测性能已达顶尖水平，但其校准性（即模型置信度是否准确）仍研究不足，而这对实际应用至关重要。本文系统评估了五种最新时序基础模型及两种竞争性基线的校准特性，涵盖模型校准度（过/欠自信）、不同预测头的影响，以及长期自回归预测下的校准表现。结果表明，时序基础模型整体上比基线模型更优，且不呈现系统性过自信或欠自信现象，这与许多深度学习模型常见的过自信问题形成鲜明对比。
原文摘要 · Abstract (English)
The recent development of foundation models for time series data has generated considerable interest in using such models across a variety of applications. Although foundation models achieve state-of-the-art predictive performance, their calibration properties remain relatively underexplored, despite the fact that calibration can be critical for many practical applications. In this paper, we investigate the calibration-related properties of five recent time series foundation models and two competitive baselines. We perform a series of systematic evaluations assessing model calibration (i.e., over- or under-confidence), effects of varying prediction heads, and calibration under long-term autoregressive forecasting. We find that time series foundation models are consistently better calibrated than baseline models and tend not to be either systematically over- or under-confident, in contrast to the overconfidence often seen in other deep learning models.
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