为什么重要 为时序异常检测提供统一分类、评测与开源基准，可参考模型选型与评估。
时间序列异常检测（TSAD）是数据挖掘中的一个基础问题，旨在识别显著偏离正常时间模式的观测。受数据量增长和应用复杂性提升驱动，近期研究产生了多样架构，包括 diffusion models、state space models 和 large language models（LLMs）。
然而，现有综述对这些新兴设计覆盖有限，并且缺乏对多变量异常、跨域统一建模、指标可靠性和当代评估挑战的统一处理。为弥补这些缺口，本综述提供 3 个核心贡献：（1）我们建立了一个精细的异常分类和反映现代进展的结构化范式分类，比较检测机制并描绘各范式的发展轨迹；（2）我们对评估方法进行了广泛分析，并进行综合实验以评估指标保真度，同时辅以包含算法、数据集和评估工具的开源基准仓库；（3）我们概述了关键开放挑战，并对动态阈值、模型可解释性、zero-shot transfer、multi-modal alignment 和 time series 的 foundational models 提供前瞻性视角。通过解决现有理论和实证限制，本综述建立了一个连贯框架，以指导未来 TSAD 研究和实际部署。
Time series anomaly detection (TSAD) is a foundational problem in data mining, aiming to identify observations that deviate significantly from nominal temporal patterns.
Driven by escalating data volumes and application complexity, recent research has yielded diverse architectures, including diffusion models, state space models, and large language models (LLMs).
However, existing surveys provide limited coverage of these emerging designs and lack a unified treatment of multivariate anomalies, cross-domain unified modeling, metric reliability, and contemporary evaluation challenges.
To bridge these gaps, this survey delivers three core contributions: (1) We establish a refined anomaly categorization and a structured taxonomy of paradigms reflecting modern advances, comparing detection mechanisms and charting developmental trajectories across paradigms.
(2) We provide an extensive analysis of evaluation methodologies and conduct comprehensive experiments to assess metric fidelity, complemented by an open-source benchmark repository encompassing algorithms, datasets, and evaluation tools.
(3) We outline key open challenges, offering forward-looking perspectives on dynamic thresholding, model interpretability, zero-shot transfer, multi-modal alignment, and foundational models for time series.
By addressing existing theoretical and empirical limitations, this survey establishes a coherent framework to guide future TSAD research and practical deployments.