Time series reasoning treats time as a first-class axis and incorporates intermediate evidence directly into the answer. This survey defines the problem and organizes the literature by reasoning topology with three families: direct reasoning in one step, linear chain reasoning with explicit intermediates, and branch-structured reasoning that explores, revises, and aggregates. The topology is crossed with the main objectives of the field, including traditional time series analysis, explanation and understanding, causal inference and decision making, and time series generation, while a compact tag set spans these axes and captures decomposition and verification, ensembling, tool use, knowledge access, multimodality, agent loops, and LLM alignment regimes. Methods and systems are reviewed across domains, showing what each topology enables and where it breaks down in faithfulness or robustness, along with curated datasets, benchmarks, and resources that support study and deployment (https://github.com/blacksnail789521/Time-Series-Reasoning-Survey). Evaluation practices that keep evidence visible and temporally aligned are highlighted, and guidance is distilled on matching topology to uncertainty, grounding with observable artifacts, planning for shift and streaming, and treating cost and latency as design budgets. We emphasize that reasoning structures must balance capacity for grounding and self-correction against computational cost and reproducibility, while future progress will likely depend on benchmarks that tie reasoning quality to utility and on closed-loop testbeds that trade off cost and risk under shift-aware, streaming, and long-horizon settings. Taken together, these directions mark a shift from narrow accuracy toward reliability at scale, enabling systems that not only analyze but also understand, explain, and act on dynamic worlds with traceable evidence and credible outcomes.

翻译：时间序列推理将时间视为首要维度，并将中间证据直接融入答案中。本综述界定了该问题，并通过推理拓扑结构对文献进行了梳理，将其划分为三类：单步直接推理、具有显式中间步骤的线性链推理，以及探索、修正与聚合的分支结构推理。该拓扑结构与本领域的主要目标交叉，包括传统时间序列分析、解释与理解、因果推断与决策制定以及时间序列生成，同时一套紧凑的标签集贯穿这些维度，涵盖了分解与验证、集成、工具使用、知识访问、多模态、智能体循环以及大语言模型对齐机制。研究回顾了跨领域的方法与系统，展示了每种拓扑结构的能力及其在忠实度或鲁棒性方面的局限，并整理了支持研究与部署的精选数据集、基准测试和资源（https://github.com/blacksnail789521/Time-Series-Reasoning-Survey）。重点强调了保持证据可见且时间对齐的评估实践，并提炼了关于根据不确定性匹配拓扑结构、通过可观测产物进行事实基础、规划数据漂移与流式处理、以及将成本与延迟视为设计预算的指导原则。我们强调，推理结构必须在事实基础与自我修正能力与计算成本及可复现性之间取得平衡，而未来的进展可能依赖于将推理质量与实用性挂钩的基准测试，以及在具备漂移感知、流式处理和长周期设置的闭环测试平台上权衡成本与风险。综上所述，这些方向标志着从狭隘的准确性向大规模可靠性的转变，使得系统不仅能分析动态世界，还能以可追溯的证据和可信的结果来理解、解释并作用于动态世界。