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

2025-06-01

AI Summary

Large language models (LLMs) remain underutilized for time-series classification (TSC) due to their limited capacity to perform structured, domain-aware reasoning over temporal data. Method: We propose ReasonTSC—a novel framework introducing the first structured, multi-step reasoning paradigm tailored to TSC. It dynamically integrates time-series features, plugin model predictions, and confidence scores into prompts, enabling backtracking, reflection, and hypothesis comparison. Furthermore, it incorporates a confidence-weighted fusion mechanism and domain-guided chain-of-thought prompting to achieve synergistic, trustworthy reasoning between LLMs and specialized classifiers. Contribution/Results: Evaluated on multiple standard TSC benchmarks, ReasonTSC significantly outperforms existing LLM-based reasoning baselines and plugin methods, demonstrating superior robustness, self-correcting capability, and calibration. It establishes a new paradigm for trustworthy, LLM-augmented time-series analysis.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

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.

Problem

• Enhancing LLM reasoning for time series classification tasks
• Overcoming limitations of text-domain reasoning in time series
• Integrating domain-specific models with LLM reasoning strategies

Innovation

• Multi-turn reasoning for time series classification
• Fused decision-making with plug-in classifiers
• Structured reasoning process with backtracking