发表机构
Siemens Data and AI Research; Nanjing University – Siemens Joint Research Center on Industrial AI（西门子数据与人工智能研究院; 南京大学-西门子工业人工智能联合研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出SGA方法，通过有向无环图刻画预测分支拓扑并整合随机性量化多步TSFM预测不确定性，实验验证其排序性能和缩放定律。
AI 中文摘要
时间序列基础模型（TSFMs）的近期出现显著提升了多步预测性能，使得在更长的未来时间范围内能够进行准确预测。然而，现有的TSFMs常常遭受显著的内在不确定性，这通常表现为在每个时间步上衍生出预测分支，并传播到后续步骤；不同的预测分支往往表现出不同的预测性能，从而削弱了TSFM预测的可信度。在本文中，我们提出了切片-图构建-对齐（SGA）方法来量化多步TSFM预测的不确定性。所提出的SGA首先使用有向无环图刻画所有潜在预测分支的拓扑结构，使得图复杂度界定了多步预测的不确定性，然后通过整合拓扑信息和TSFM固有的随机性来精确度量图复杂度。在11个TSFMs和27个数据集上进行的实验结果表明：（i）SGA在使用不确定性估计对预测误差进行排序时取得了最佳性能；（ii）与现有的不确定性量化（UQ）方法相比，SGA具有更广泛且更精确的采样覆盖范围，从而推导出一种与现有方法根本不同的量化机制；（iii）TSFMs的更大模型规模与多步预测的较低不确定性估计相关，这为多步TSFM预测的不确定性量化提供了另一个经验缩放定律。
英文摘要
The recent emergence of Time Series Foundation Models (TSFMs) has significantly advanced multi-step forecasting performance, enabling accurate predictions over extended future horizons. However, existing TSFMs often suffer from significantly inherent uncertainty, which typically manifests as derived forecast branches emerging at each time step and spreading to subsequent steps; different forecast branches often exhibit varying forecasting performance, thereby undermining the credibility of TSFM forecasts. In this paper, we propose the Slicing-Graphing-Alignment (SGA) method to quantify the uncertainty of multi-step TSFM forecasts. The proposed SGA first characterizes the topology of all potential forecast branches using a directed acyclic graph, such that the graph complexity bounds the uncertainty of multi-step forecasts, and then precisely measures the graph complexity by integrating both topological information and TSFM-inherent stochasticity. Experimental results conducted on 11 TSFMs and 27 datasets demonstrate that (i) SGA achieves the best performance when ranking predictive errors with uncertainty estimates; (ii) SGA works with a more extensive and more precise sampling coverage than those of existing UQ methods, deriving a quantification mechanism fundamentally different from those of established ones; and (iii) larger model scales of TSFMs correlate with lower uncertainty estimates of multi-step forecasts, suggesting another empirical scaling law for uncertainty quantification of multi-step TSFM forecasts.
