In this study, we address the language-guided time-series event detection problem: given a natural-language event description and a time series, the system returns event occurrences as temporal spans. Language-guided event detection leverages domain knowledge expressed in natural language to enable zero-shot detection without domain-specific fine-tuning. We introduce GroundingMOMENT, a zero-shot cross-modal grounding framework that aligns a time-series foundation model with a language model. GroundingMOMENT concurrently localizes both complex, high-level event occurrences and their constituent domain-agnostic structural primitives (e.g., spikes, plateaus). We further introduce GroundTSSyn, a scalable pipeline for generating synthetic time-series grounding data, and release GroundTSBench, the first open-source benchmark for language-guided time-series grounding. Trained exclusively on GroundTSBench, GroundingMOMENT outperforms time-series multimodal large language models and achieves fast and accurate event detection. Case studies further demonstrate its zero-shot generalizability to real-world data from diverse domains.

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.