We introduce TimeCopilot, the first open-source agentic framework for forecasting that combines multiple Time Series Foundation Models (TSFMs) with Large Language Models (LLMs) through a single unified API. TimeCopilot automates the forecasting pipeline: feature analysis, model selection, cross-validation, and forecast generation, while providing natural language explanations and supporting direct queries about the future. The framework is LLM-agnostic, compatible with both commercial and open-source models, and supports ensembles across diverse forecasting families. Results on the large-scale GIFT-Eval benchmark show that TimeCopilot achieves state-of-the-art probabilistic forecasting performance at low cost. Our framework provides a practical foundation for reproducible, explainable, and accessible agentic forecasting systems.

翻译：本文提出TimeCopilot——首个开源的智能体化预测框架，通过统一API将多个时序基础模型（TSFMs）与大型语言模型（LLMs）相结合。TimeCopilot实现了预测流程的自动化：特征分析、模型选择、交叉验证与预测生成，同时提供自然语言解释并支持对未来趋势的直接查询。该框架兼容各类LLM（商业与开源模型均可），支持跨不同预测模型族的集成方法。在大规模GIFT-Eval基准测试中，TimeCopilot以较低成本实现了最先进的概率预测性能。本框架为可复现、可解释、易访问的智能体预测系统奠定了实践基础。