构建可解释可信的时间序列推理框架，推动从模式识别到智能推断的跨越。
Towards Interpretable and Trustworthy Time Series Reasoning: A BlueSky Vision
- 融合时序理解与多步推理，建立可信赖评估体系。
- 引入多智能体协作与多模态信息，提升系统级推理能力。
- 适合关注时间序列智能分析的科研与工程人员。
时间序列推理正成为时序分析的新前沿，旨在超越模式识别，实现显式、可解释且可信的推断。本文提出一个蓝海愿景，涵盖两个互补方向：一是夯实时间序列推理基础，聚焦全面的时序理解、结构化多步推理和忠实的评估框架；二是推进系统级推理，突破仅依赖语言解释的局限，引入多智能体协作、多模态上下文及检索增强方法。二者共同构成一个灵活可扩展的框架，致力于在多元领域实现可解释且可信的时序智能。
原文摘要 · Abstract (English)
Time series reasoning is emerging as the next frontier in temporal analysis, aiming to move beyond pattern recognition towards explicit, interpretable, and trustworthy inference. This paper presents a BlueSky vision built on two complementary directions. One builds robust foundations for time series reasoning, centered on comprehensive temporal understanding, structured multi-step reasoning, and faithful evaluation frameworks. The other advances system-level reasoning, moving beyond language-only explanations by incorporating multi-agent collaboration, multi-modal context, and retrieval-augmented approaches. Together, these directions outline a flexible and extensible framework for advancing time series reasoning, aiming to deliver interpretable and trustworthy temporal intelligence across diverse domains.
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