TimeCopilot
This provides a practical foundation for reproducible, explainable, and accessible agentic forecasting systems, though it appears incremental as it combines existing components in a novel framework.
The authors tackled the problem of automating time series forecasting by introducing TimeCopilot, an open-source agentic framework that combines multiple Time Series Foundation Models with Large Language Models through a unified API. Results on the GIFT-Eval benchmark show it achieves state-of-the-art probabilistic forecasting performance at low cost.
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.