面向时间序列基础模型的因果分析

中文摘要 AI 辅助

从定制化时间序列模型向时间序列基础模型的转变，改变了模型与应用的关系，从一对一变为一对多。这种转变带来了集中风险，因为许多潜在高风险的预测应用都暴露于单个时间序列基础模型的相同偏差和故障模式下。与此同时，这种集中化也使模型开发与验证实现了规模经济。本研究探讨如何在部署前识别时间序列基础模型的偏差与故障模式。我们提出一种因果分析框架，用于研究时间序列基础模型保留时间序列模式的能力。为实现这一目标，我们对参数化合成时间序列生成器进行干预，并在其他条件相同（ceteris paribus）的情况下测量模型输出的相应变化。我们将该因果分析框架应用于Chronos-2和TimesFM-2.5，并在六种不同的时间序列模式上对其进行测试。我们为趋势和谐波振荡模式找到了安全配置。结果还表明，两种模型都存在高估持续性的偏差；针对制度切换模式，两种模型均会出现突发故障；针对能量释放模式，TimesFM-2.5会出现故障。我们对两种模型原始研究的审查表明，这些发现可能可由预训练所用数据解释。本研究最后提出了进一步模型开发的建议、针对特定应用的模型选择建议，以及对局限性和未来研究方向的讨论。

英文摘要

Transitioning from bespoke time series models towards time series foundation models changes the relationship of model and application from one-to-one to one-to-many. This shift introduces concentration risk as many, potentially high-risk, forecasting applications are exposed to the same biases and failure modes of a single time series foundation model. At the same time, this centralization allows for economies of scale in model development and validation. In this study we investigate how biases and failure modes of time series foundation models can be identified before deployment. We propose a causal analysis framework to investigate the ability of a time series foundation model to preserve time series patterns. To achieve this, we intervene on parameterized synthetic time series generators and measure the corresponding change in model output under ceteris paribus conditions. We apply our causal analysis framework to Chronos-2 and TimesFM-2.5 and test them across six distinct time series patterns. We find safe configurations for trend and harmonic oscillation patterns. The results also indicate a bias in both models towards overestimating persistence, sudden failures for both models against the regime switch pattern and failure for TimesFM-2.5 against the energy-release pattern. Our review of the original works for both models indicates that the findings might be explained by the data used for pretraining. We conclude our study with suggestions for further model development, recommendations for application-specific model selection, and a discussion of limitations and further research directions.

发表机构

• Faculty of Behavioral, Management and Social Sciences, University of Twente（特温特大学行为、管理与社会科学学院）
• European Central Bank（欧洲中央银行）

机构由 AI 辅助整理，请以论文原文为准。