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

• URL: http://arxiv.org/abs/2410.04047v3
• Date: Wed, 12 Feb 2025 00:23:36 GMT
• Title: Multi-Step Time Series Inference Agent for Reasoning and Automated Task Execution
• Authors: Wen Ye, Yizhou Zhang, Wei Yang, Defu Cao, Lumingyuan Tang, Jie Cai, Yan Liu,
• Abstract summary: We propose a novel task: multi-step time series inference that demands both compositional reasoning and precision of time series analysis.<n>By integrating in-context learning, self-correction, and program-aided execution, our proposed approach ensures accurate and interpretable results.
• Score: 19.64976935450366
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: 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.

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