Multi-Step Time Series Inference Agent for Reasoning and Automated Task Execution

Time series analysis is crucial in real-world applications, yet traditional methods focus on isolated tasks only, and recent studies on time series reasoning remain limited to simple, single-step inference constrained to natural language answer. In this work, we propose a practical novel task: multi-step time series inference that demands both compositional reasoning and computation precision of time series analysis. To address such challenge, we propose a simple but effective program-aided inference agent that leverages LLMs' reasoning ability to decompose complex tasks into structured execution pipelines. By integrating in-context learning, self-correction, and program-aided execution, our proposed approach ensures accurate and interpretable results. To benchmark performance, we introduce a new dataset and a unified evaluation framework with task-specific success criteria. Experiments show that our approach outperforms standalone general purpose LLMs in both basic time series concept understanding as well as multi-step time series inference task, highlighting the importance of hybrid approaches that combine reasoning with computational precision.

@article{ye2025_2410.04047,
  title={ Multi-Step Time Series Inference Agent for Reasoning and Automated Task Execution },
  author={ Wen Ye and Yizhou Zhang and Wei Yang and Defu Cao and Lumingyuan Tang and Jie Cai and Yan Liu },
  journal={arXiv preprint arXiv:2410.04047},
  year={ 2025 }
}