Position: Universal Time Series Foundation Models Rest on a Category Error

2026-02-05

AI Summary

This work argues that monolithic universal time series foundation models suffer from a category error by overlooking fundamental differences in data-generating mechanisms across domains, leading to severely limited generalization under distributional shifts. To address this, the paper proposes replacing such models with a causal control agent that orchestrates a hierarchy of specialized solvers via external contextual routing. This architecture combines frozen domain experts with lightweight, just-in-time adapters to enable rapid adaptation to intervention-driven abrupt changes. Grounded in the newly introduced theoretical limit of the “autoregressive blind spot,” the study redefines evaluation benchmarks around adaptation speed to distributional shifts, offering a novel paradigm and theoretical foundation for building robust, adaptive time series systems.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Multiagent Systems: Adversarial Agents

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• Universal Foundation Models
• Time Series
• Category Error
• Distributional Drift
• Generative Processes

Innovation

• Causal Control Agent
• Autoregressive Blindness Bound
• Distributional Drift
• Time Series Foundation Models
• Just-in-Time Adaptation