Position: Universal Time Series Foundation Models Rest on a Category Error
This position paper critiques a foundational approach in time series modeling, potentially impacting researchers and practitioners by highlighting limitations in current methods and suggesting a shift towards more robust, control-theoretic systems.
The paper argues that universal time series foundation models are flawed due to a category error, as they fail to generalize across different generative processes like finance and fluid dynamics, leading to poor performance under distributional drift. It proposes a Causal Control Agent paradigm to replace universality, advocating for benchmarks focused on drift adaptation speed instead of zero-shot accuracy.
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.