{"total":31,"rows":[{"id":"2211.05244","title":"Deep Learning for Time Series Anomaly Detection: A Survey","created":"2022-11-09","cited_by_count":131,"area":null},{"id":"2506.18046","title":"TAB: Unified Benchmarking of Time Series Anomaly Detection Methods","created":"2025-06-22","cited_by_count":60,"area":null},{"id":"2207.12201","title":"Calibrated One-class Classification for Unsupervised Time Series Anomaly Detection","created":"2022-07-25","cited_by_count":40,"area":null},{"id":"2303.01272","title":"Navigating the Metric Maze: A Taxonomy of Evaluation Metrics for Anomaly Detection in Time Series","created":"2023-03-02","cited_by_count":24,"area":null},{"id":"2405.02678","title":"Position: Quo Vadis, Unsupervised Time Series Anomaly Detection?","created":"2024-05-04","cited_by_count":17,"area":null},{"id":"2211.09224","title":"Are we certain it's anomalous?","created":"2022-11-16","cited_by_count":13,"area":null},{"id":"2408.13082","title":"Multivariate Time-Series Anomaly Detection based on Enhancing Graph Attention Networks with Topological Analysis","created":"2024-08-23","cited_by_count":11,"area":null},{"id":"2305.14765","title":"Masked Bayesian Neural Networks : Theoretical Guarantee and its Posterior Inference","created":"2023-05-24","cited_by_count":9,"area":null},{"id":"2308.12551","title":"A Co-training Approach for Noisy Time Series Learning","created":"2023-08-24","cited_by_count":8,"area":null},{"id":"2510.16014","title":"STAR: Boosting Time Series Foundation Models for Anomaly Detection through State-aware Adapter","created":"2025-10-15","cited_by_count":7,"area":null},{"id":"2502.13256","title":"Cyber-Physical Systems Security: A Comprehensive Review of Anomaly Detection Techniques","created":"2025-02-18","cited_by_count":4,"area":null},{"id":"2112.06247","title":"DeepFIB: Self-Imputation for Time Series Anomaly Detection","created":"2021-12-12","cited_by_count":2,"area":null},{"id":"2208.01841","title":"Robust Learning of Deep Time Series Anomaly Detection Models with Contaminated Training Data","created":"2022-08-03","cited_by_count":2,"area":null},{"id":"2411.10918","title":"INVARLLM: LLM-assisted Physical Invariant Extraction for Cyber-Physical Systems Anomaly Detection","created":"2024-11-17","cited_by_count":2,"area":null},{"id":"2606.01300","title":"ChronosAD: Leveraging Time Series Foundation Models for Accurate Anomaly Detection","created":"2026-05-31","cited_by_count":2,"area":null},{"id":"2211.13577","title":"Learning Invariant Rules from Data for Interpretable Anomaly Detection","created":"2022-11-24","cited_by_count":1,"area":null},{"id":"2410.21322","title":"Angel or Devil: Discriminating Hard Samples and Anomaly Contaminations for Unsupervised Time Series Anomaly Detection","created":"2024-10-26","cited_by_count":1,"area":null},{"id":"2511.18739","title":"A Problem-Oriented Taxonomy of Evaluation Metrics for Time Series Anomaly Detection","created":"2025-11-24","cited_by_count":1,"area":null},{"id":"2604.13924","title":"ASTER: Latent Pseudo-Anomaly Generation for Unsupervised Time-Series Anomaly Detection","created":"2026-04-15","cited_by_count":1,"area":null},{"id":"2208.09240","title":"An Unsupervised Short- and Long-Term Mask Representation for Multivariate Time Series Anomaly Detection","created":"2022-08-19","cited_by_count":0,"area":null},{"id":"2301.13031","title":"BSSAD: Towards A Novel Bayesian State-Space Approach for Anomaly Detection in Multivariate Time Series","created":"2023-01-30","cited_by_count":0,"area":null},{"id":"2303.03324","title":"Time series anomaly detection with reconstruction-based state-space models","created":"2023-03-06","cited_by_count":0,"area":null},{"id":"2305.16907","title":"CyPhERS: A Cyber-Physical Event Reasoning System providing real-time situational awareness for attack and fault response","created":"2023-05-26","cited_by_count":0,"area":null},{"id":"2312.10968","title":"PARs: Predicate-based Association Rules for Efficient and Accurate Model-Agnostic Anomaly Explanation","created":"2023-12-18","cited_by_count":0,"area":null},{"id":"2504.04374","title":"iADCPS: Time Series Anomaly Detection for Evolving Cyber-physical Systems via Incremental Meta-learning","created":"2025-04-06","cited_by_count":0,"area":null},{"id":"2512.11298","title":"SRLR: Symbolic Regression based Logic Recovery to Counter Programmable Logic Controller Attacks","created":"2025-12-12","cited_by_count":0,"area":null},{"id":"2603.12015","title":"Flowcean - Model Learning for Cyber-Physical Systems","created":"2026-03-12","cited_by_count":0,"area":null},{"id":"2603.13895","title":"MO-SAE:Multi-Objective Stacked Autoencoders Optimization for Edge Anomaly Detection","created":"2026-03-14","cited_by_count":0,"area":null},{"id":"2607.22286","title":"An Insight on Evaluation Metrics Under the Imbalanced Case of Anomaly Detection","created":"2026-07-24","cited_by_count":0,"area":null},{"id":"2609.34161","title":"GT-PSSM: Unified Probabilistic Framework for Stochastic Dynamics Modeling and Dependency Learning in Multivariate Time Series Anomaly Detection","created":"2026-09-28","cited_by_count":0,"area":null},{"id":"2609.34804","title":"Physics-Attested Federated Learning: Securing Collaborative Anomaly Detection in Critical Water Infrastructure","created":"2026-09-28","cited_by_count":0,"area":null}],"next_cursor":null}