TimeRouter: New Framework for Efficient Routing of Time-Series Foundation Models
Researchers have developed TimeRouter, a lightweight routing framework that automatically selects the best time-series forecasting model from a pool of pretrained models without requiring expensive language model inference. The framework uses learned routing, selective gating, and ensemble fallback mechanisms to achieve state-of-the-art performance on benchmark evaluations. This addresses a key challenge in building agentic time-series systems where different models excel in different forecasting scenarios.
TimeRouter is a new routing framework designed to efficiently select among multiple time-series foundation models (TSFMs) for forecasting tasks. The system addresses a fundamental problem: while TSFMs are increasingly used as predictive experts in automated time-series systems, no single model consistently performs best across all forecasting scenarios, and existing approaches often rely on expensive language model-based controllers. TimeRouter combines three mechanisms—a learned routing head, a selective gate, and an ensemble fallback—to enable adaptive expert selection without invoking an LLM at inference time, significantly reducing computational overhead. The framework achieved state-of-the-art results on the GIFT-EVAL leaderboard with an LB MASE score of 0.6765. Ablation studies revealed that pool composition and selective gating are critical design factors. The authors have released their code publicly, positioning TimeRouter as a modular component for future agentic time-series systems.
What's missing
The paper does not discuss computational cost comparisons between TimeRouter and LLM-based routing approaches, nor does it provide runtime benchmarks or memory requirements. Additionally, the generalizability of the approach to time-series domains beyond those in GIFT-EVAL is not explicitly addressed.
What different sources said
- arXiv cs.LGCenter
TimeRouter: Efficient and Adaptive Routing of Time-Series Foundation Models
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