Enhancing LLM Reasoning for Time Series Classification by Tailored Thinking and Fused Decision

• URL: http://arxiv.org/abs/2506.00807v1
• Date: Sun, 01 Jun 2025 03:15:54 GMT
• Title: Enhancing LLM Reasoning for Time Series Classification by Tailored Thinking and Fused Decision
• Authors: Jiahui Zhou, Dan Li, Lin Li, Zhuomin Chen, Shunyu Wu, Haozheng Ye, Jian Lou, Costas J. Spanos,
• Abstract summary: ReasonTSC is a framework designed to leverage LLM reasoning for time series classification.<n>It steers the model to think over the essential characteristics of time series data.<n>It integrates predictions and confidence scores from plug-in classifiers, e.g., domain-specific time series models, as in-context examples.
• Score: 8.256998757769322
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The reasoning capabilities of large language models (LLMs) have significantly advanced their performance by enabling in-depth understanding of diverse tasks. With growing interest in applying LLMs to the time series domain, this has proven nontrivial, as evidenced by the limited efficacy of straightforwardly adapting text-domain reasoning techniques. Although recent work has shown promise in several time series tasks, further leveraging advancements in LLM reasoning remains under-explored for time series classification (TSC) tasks, despite their prevalence and significance in many real-world applications. In this paper, we propose ReasonTSC, a novel framework designed to effectively leverage LLM reasoning for time series classification through both a multi-turn reasoning and a fused decision-making strategy tailored to TSC. Rather than straightforwardly applying existing reasoning techniques or relying solely on LLMs' built-in reasoning capabilities, ReasonTSC first steers the model to think over the essential characteristics of time series data. Next, it integrates predictions and confidence scores from plug-in classifiers, e.g., domain-specific time series models, as in-context examples. Finally, ReasonTSC guides the LLM through a structured reasoning process: it evaluates the initial assessment, backtracks to consider alternative hypotheses, and compares their merits before arriving at a final classification. Extensive experiments and systematic ablation studies demonstrate that ReasonTSC consistently outperforms both existing time series reasoning baselines and plug-in models, and is even capable of identifying and correcting plug-in models' false predictions.

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