Time Series Event Detection (TSED) aims to localize semantically meaningful events in time series data, with critical applications in high-stakes domains. Unlike statistical anomalies, events are often defined by natural-language descriptions with internal temporal-logic structures across multiple physical channels. However, in real-world settings, dense event annotations are expensive to obtain, making purely supervised learning difficult. We introduce Language-guided TSED, a setting where a model is given textual event descriptions and must ground them to intervals in multivariate signals with little or no labeled data. To address this problem, we propose Event Logic Tree (ELT), a knowledge representation framework that converts linguistic descriptions into structured temporal logic over signal primitives. Building on ELT, we present SELA, a neuro-symbolic VLM agent framework that iteratively grounds primitives from signal visualizations and composes them under ELT constraints, producing both event intervals and faithful tree-structured explanations. We further release a real-world benchmark across energy and climate domains with expert knowledge and annotations. Experiments show that SELA improves over supervised fine-tuning and existing zero/few-shot time series reasoning baselines.

翻译：时间序列事件检测（TSED）旨在定位时间序列数据中具有语义意义的事件，在高风险领域具有关键应用。与统计异常不同，事件通常由包含跨多个物理通道的内部时序逻辑结构的自然语言描述来定义。然而，在现实场景中，密集的事件标注成本高昂，这使得纯监督学习变得困难。我们提出语言引导的TSED这一设定：在此设定中，模型接收文本事件描述，并需在极少或无标注数据的情况下，将这些描述与多元信号中的时间区间进行对应。为解决该问题，我们提出事件逻辑树（ELT）——一种将语言描述转换为信号基元上结构化时序逻辑的知识表示框架。基于ELT，我们进一步提出SELA——一种神经符号化VLM智能体框架，该框架通过迭代从信号可视化中基元定位，并在ELT约束下组合这些基元，从而生成事件区间及忠实于树结构的可解释输出。我们还发布了涵盖能源与气候领域的真实世界基准数据集，包含专家知识与标注。实验表明，SELA在监督微调及现有零/少样本时间序列推理基线方法上均取得了性能提升。