AIBullisharXiv – CS AI · 5h ago7/10
🧠
Training-Free Time Series Classification via In-Context Reasoning with LLM Agents
Researchers introduce FETA, a multi-agent framework that enables large language models to classify time series data without any training or fine-tuning. The system decomposes multivariate time series into individual channels, retrieves similar labeled examples, and uses LLM reasoning to make predictions with confidence scores, achieving competitive accuracy on benchmark datasets.