arXiv:2606.11625v1 Announce Type: new Abstract: Time-series foundation models (TSFMs) are increasingly explored as predictive experts within emerging agentic time-series systems. However, TSFMs exhibit heterogeneous inductive biases, and no single model consistently dominates across forecasting regimes, making expert selection a critical challenge. Existing systems often delegate this decision to LLM-based controllers, incurring substantial inference overhead. We present TimeRouter, an efficient routing framework that leverages empirical complementarity across a pool of pretrained TSFMs throug
The proliferation of time-series foundation models demands more efficient management methods, pushing for breakthroughs in adaptive routing to optimize performance and resource use.
This development allows for more efficient and adaptable utilization of advanced AI models in time-series forecasting, reducing computational overhead and improving accuracy in critical applications.
The way agentic time-series systems select and apply predictive models becomes significantly more efficient, reducing reliance on expensive LLM-based controllers for model routing.
- · AI platform providers
- · Enterprises reliant on time-series forecasting
- · Developers of specialized TSFMs
- · Inefficient LLM-based routing solutions
- · Systems with monolithic forecasting models
Improved performance and cost-efficiency in systems leveraging multiple time-series foundation models.
Accelerated adoption of agentic AI systems due to lower operational costs and greater predictive accuracy.
Enhanced automation and decision-making across industries, from finance to logistics, as forecasting becomes more robust and accessible.
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Read at arXiv cs.LG