时间序列通用模型是伪命题，需用因果控制代理替代。
Position: Universal Time Series Foundation Models Rest on a Category Error
- 将时间序列视为统一模态是根本错误，不同领域生成机制不兼容。
- 历史依赖模型无法预测干预引发的突变，存在理论极限。
- 提倡用外部上下文驱动的专用求解器层级，提升适应速度。
本文指出，追求‘时间序列通用基础模型’存在根本性范畴错误，误将结构化的容器当作语义模态。由于时间序列包含不相容的生成过程（如金融与流体动力学），单一模型会退化为昂贵的‘通用滤波器’，在分布漂移下无法泛化。我们提出‘自回归盲区定理’，证明仅依赖历史的模型无法预测由干预引发的制度性转变。建议以‘因果控制代理’范式取代通用性，即通过外部上下文协调专用求解器层级——从冻结的领域专家到轻量级即时适配器。最后呼吁将评估基准从‘零样本准确率’转向‘漂移适应速度’，以优先发展具备控制理论鲁棒性的系统。
原文摘要 · Abstract (English)
This position paper argues that the pursuit of "Universal Foundation Models for Time Series" rests on a fundamental category error, mistaking a structural Container for a semantic Modality. We contend that because time series hold incompatible generative processes (e.g., finance vs. fluid dynamics), monolithic models degenerate into expensive "Generic Filters" that fail to generalize under distributional drift. To address this, we introduce the "Autoregressive Blindness Bound," a theoretical limit proving that history-only models cannot predict intervention-driven regime shifts. We advocate replacing universality with a Causal Control Agent paradigm, where an agent leverages external context to orchestrate a hierarchy of specialized solvers, from frozen domain experts to lightweight Just-in-Time adaptors. We conclude by calling for a shift in benchmarks from "Zero-Shot Accuracy" to "Drift Adaptation Speed" to prioritize robust, control-theoretic systems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。