KairosAgent: Agentic Time Series Forecasting with Fused Semantic Reasoning

Researchers introduce KairosAgent, an agentic framework combining large language models with time series foundation models to improve multimodal forecasting across domains. The system uses semantic reasoning from LLMs fused with numerical forecasting capabilities, achieving superior zero-shot performance through reinforcement learning and structured tool integration.

KairosAgent addresses a fundamental limitation in current forecasting systems: the inability to simultaneously handle semantic reasoning and precise numerical prediction. Traditional time series foundation models excel at pattern recognition but lack contextual understanding, while large language models understand semantics but struggle with quantitative accuracy. This research bridges that gap through an agentic architecture that treats forecasting as a problem requiring multiple specialized tools working in concert.

The approach reflects broader trends in AI research toward modular, tool-using systems that orchestrate different model capabilities. Rather than building monolithic models from scratch, KairosAgent leverages existing pretrained models—both LLMs and TSFMs—and enhances them through dynamic tool invocation and semantic fusion. The reinforcement learning paradigm from forecasting introduces a practical training methodology that aligns model behavior with actual prediction quality rather than traditional supervised learning objectives.

For practitioners in quantitative finance, supply chain management, and climate forecasting, this framework offers practical improvements in interpretability and accuracy. The zero-shot performance capabilities suggest reduced need for extensive domain-specific fine-tuning, lowering implementation costs. The emphasis on future-oriented semantic reasoning enables models to incorporate narrative context—earnings reports, policy announcements, market conditions—directly into numerical predictions.

The open research direction signals growing convergence between large language model capabilities and specialized domain modeling. Future work likely focuses on scaling this framework to real-time financial data streams and validating performance across additional domains beyond the current experimental scope.

• →KairosAgent combines LLM semantic reasoning with time series forecasting through dynamic tool integration for multimodal predictions
• →The framework achieves superior zero-shot forecasting performance without extensive domain-specific fine-tuning
• →Reinforcement learning from forecasting with multi-turn refinement enables iterative improvement of reasoning quality
• →Fusion of textual context and numerical analysis improves both interpretability and accuracy of future predictions
• →Architecture leverages pretrained models efficiently rather than training foundation models from scratch