Recent advancements in time-series anomaly detection have relied on deep learning models to handle the diverse behaviors of time-series data. However, these models often suffer from unstable training and require extensive hyperparameter tuning, leading to practical limitations. Although foundation models present a potential solution, their use in time series is limited. To overcome these issues, we propose an innovative image-based, training-free time-series anomaly detection (ITF-TAD) approach. ITF-TAD converts time-series data into images using wavelet transform and compresses them into a single representation, leveraging image foundation models for anomaly detection. This approach achieves high-performance anomaly detection without unstable neural network training or hyperparameter tuning. Furthermore, ITF-TAD identifies anomalies across different frequencies, providing users with a detailed visualization of anomalies and their corresponding frequencies. Comprehensive experiments on five benchmark datasets, including univariate and multivariate time series, demonstrate that ITF-TAD offers a practical and effective solution with performance exceeding or comparable to that of deep models.
A Comparative Study of Adaptation Strategies for Time Series Foundation Models in Anomaly Detection
Department of Electronic Engineering, Hanyang University, Seoul, Korea（电子工程系，翰阳大学，首尔，韩国）；Department of Artificial Intelligence, Hanyang University, Seoul, Korea（人工智能系，翰阳大学，首尔，韩国）
Foundation Models for Anomaly Detection: Vision and Challenges
School of Computing Technologies, RMIT University（计算技术学院，皇家墨尔本理工大学）；STEM, the University of South Australia（STEM，南澳大利亚大学）；School of Computing and Information Systems, University of Melbourne（计算与信息学院，墨尔本大学）；CSIRO's Data61（CSIRO的数据61）
Towards Foundation Models for Zero-Shot Time Series Anomaly Detection: Leveraging Synthetic Data and Relative Context Discrepancy
Department of Industrial Engineering, Tsinghua University, Beijing, China（清华大学工业工程系）；Datadog AI Research, Paris, France. This work was completed prior to joining Datadog（Datadog AI 研究院）；Lab, Huawei Technologies, ShenZhen, China（华为技术2012实验室）
TimeRadar: A Domain-Rotatable Foundation Model for Time Series Anomaly Detection
School of Computing and Information Systems（计算与信息系）；Singapore Management University（新加坡管理大学）；Chongqing Institute of Green and Intelligent Technology（绿色与智能技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；State Information Center（信息中心）
Leveraging Exogenous Signals for Hydrology Time Series Forecasting
College of Computing（计算学院）；Georgia Institute of Technology（佐治亚理工学院）；University of Virginia（弗吉尼亚大学）；School of Data Science（数据科学学院）；Computer Science（计算机科学）；Biocomplexity Institute（生物复杂性研究所）
Is Real-World Training Data Necessary for Generalist Graph Anomaly Detection?
School of Information and Communication Technology, Griffith University（格里菲斯大学信息与通信技术学院）；School of Computer Science and Technology, Tongji University（同济大学计算机科学与技术学院）
Leveraging healthy population variability in deep learning unsupervised anomaly detection in brain FDG PET
Sorbonne Université（索邦大学）；Institut du Cerveau - Paris Brain Institute - ICM（大脑研究所 - 巴黎大脑研究所 - ICM）；CNRS（法国国家科学研究中心）；Inria（法国国家信息与自动化研究所）；Inserm（法国国家健康与医学研究院）；AP-HP（巴黎公共医疗集团）；Hôpital de la Pitié Salpêtrière（皮蒂-萨尔皮特里耶医院）
针对脑部 FDG PET 无监督异常检测中残差图像方法的局限性，提出受 Z-scores 启发、利用健康人群变异性的新方法，在 ADNI 数据库上证明其能准确识别阿尔茨海默病相关异常。
ICLAD: In-Context Learning for Unified Tabular Anomaly Detection Across Supervision Regimes
Department of Electrical and Computer Engineering, McGill University, Canada（麦吉尔大学电气与计算机工程系，加拿大）；Mila - Quebec Artificial Intelligence Institute, Montreal, Canada（魁北克人工智能研究所，蒙特利尔，加拿大）
Bridging Vision Foundation Model Priors with CLIP for Spatial-aware Few-shot Anomaly Detection in Medical Images
The Chinese University of Hong Kong（香港中文大学）；The University of Hong Kong（香港大学）；Institute of Medical Intelligence and XR, The Chinese University of Hong Kong（香港中文大学医学智能与扩展现实研究所）
Constrained Concealment Attacks against Reconstruction-based Anomaly Detectors in Industrial Control Systems
CISPA Helmholtz Center for Information Security（CISPA 信息安全亥姆霍兹中心）；Saarland University（萨尔兰大学）；Delft University of Technology（代尔夫特理工大学）；Singapore University of Technology and Design（新加坡科技设计大学）；Politecnico di Milano（米兰理工大学）
Leveraging Time-Series Foundation Model for Subsurface Well Logs Prediction and Anomaly Detection
King Fahd University of Petroleum and Minerals（法赫德石油和矿产大学）；Center for Integrative Petroleum Research（综合石油研究中心）；College of Petroleum Engineering and Geosciences（石油工程与地球科学学院）
