Training-Free Time Series Classification via In-Context Reasoning with LLM Agents

• URL: http://arxiv.org/abs/2510.05950v1
• Date: Tue, 07 Oct 2025 14:07:43 GMT
• Title: Training-Free Time Series Classification via In-Context Reasoning with LLM Agents
• Authors: Songyuan Sui, Zihang Xu, Yu-Neng Chuang, Kwei-Herng Lai, Xia Hu,
• Abstract summary: Time series classification (TSC) spans diverse application scenarios, yet labeled data are often scarce.<n>We propose FETA, a multi-agent framework for training-free TSC via exemplar-based in-context reasoning.
• Score: 29.14242392533328
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Time series classification (TSC) spans diverse application scenarios, yet labeled data are often scarce, making task-specific training costly and inflexible. Recent reasoning-oriented large language models (LLMs) show promise in understanding temporal patterns, but purely zero-shot usage remains suboptimal. We propose FETA, a multi-agent framework for training-free TSC via exemplar-based in-context reasoning. FETA decomposes a multivariate series into channel-wise subproblems, retrieves a few structurally similar labeled examples for each channel, and leverages a reasoning LLM to compare the query against these exemplars, producing channel-level labels with self-assessed confidences; a confidence-weighted aggregator then fuses all channel decisions. This design eliminates the need for pretraining or fine-tuning, improves efficiency by pruning irrelevant channels and controlling input length, and enhances interpretability through exemplar grounding and confidence estimation. On nine challenging UEA datasets, FETA achieves strong accuracy under a fully training-free setting, surpassing multiple trained baselines. These results demonstrate that a multi-agent in-context reasoning framework can transform LLMs into competitive, plug-and-play TSC solvers without any parameter training. The code is available at https://github.com/SongyuanSui/FETATSC.

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