用大模型推理能力解析时间序列，揭示真实因果关系。
Toward Reasoning-Centric Time-Series Analysis
- 将时间序列分析从模式识别转为因果推理任务
- 强调可解释性与上下文感知，提升真实场景适应性
- 适合关注可解释性与复杂系统理解的研究者
传统时间序列分析长期依赖模式识别，基于静态且成熟的基准数据集。然而在现实场景中，政策变化、人类行为适应及意外事件频发，仅分析表面趋势已不足以为继。大语言模型（LLMs）的兴起为整合多模态输入提供了新可能。但随着其广泛应用，必须审慎思考为何使用以及如何有效利用。现有大多数基于LLM的方法仍仅依赖其数值回归能力，忽视了深层推理潜力。本文主张将时间序列分析重新定位为以推理为核心的任务，优先考虑因果结构与可解释性。这一转变使分析更贴近人类认知，能够在复杂真实环境中提供透明、上下文敏感的洞察。
原文摘要 · Abstract (English)
Traditional time series analysis has long relied on pattern recognition, trained on static and well-established benchmarks. However, in real-world settings -- where policies shift, human behavior adapts, and unexpected events unfold -- effective analysis must go beyond surface-level trends to uncover the actual forces driving them. The recent rise of Large Language Models (LLMs) presents new opportunities for rethinking time series analysis by integrating multimodal inputs. However, as the use of LLMs becomes popular, we must remain cautious, asking why we use LLMs and how to exploit them effectively. Most existing LLM-based methods still employ their numerical regression ability and ignore their deeper reasoning potential. This paper argues for rethinking time series with LLMs as a reasoning task that prioritizes causal structure and explainability. This shift brings time series analysis closer to human-aligned understanding, enabling transparent and context-aware insights in complex real-world environments.
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