整合多种时间序列模型与大模型，一键生成可解释的未来预测。
TimeCopilot
- 用统一接口融合时间序列基础模型与大语言模型，自动完成预测全流程。
- 在GIFT-Eval基准上实现顶尖概率预测性能，成本极低。
- 支持自然语言提问和解释，适合需要可复现预测的科研与业务场景。
我们提出TimeCopilot，首个开源的智能预测框架，通过单一统一API将多种时间序列基础模型（TSFMs）与大语言模型（LLMs）结合。该框架自动化完成特征分析、模型选择、交叉验证和预测生成，并提供自然语言解释，支持对未来的直接查询。框架对LLM无特定依赖，兼容商业与开源模型，支持跨不同预测族的集成。在大规模GIFT-Eval基准上的结果表明，TimeCopilot以低成本实现顶尖的概率预测性能。本框架为可复现、可解释、易访问的智能预测系统提供了实用基础。
原文摘要 · Abstract (English)
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.
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