{"total":104,"rows":[{"id":"2202.07125","title":"Transformers in Time Series: A Survey","created":"2022-02-15","cited_by_count":497,"area":null},{"id":"2110.02642","title":"Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy","created":"2021-10-06","cited_by_count":394,"area":null},{"id":"2307.03759","title":"A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection","created":"2023-07-07","cited_by_count":145,"area":null},{"id":"2211.05244","title":"Deep Learning for Time Series Anomaly Detection: A Survey","created":"2022-11-09","cited_by_count":131,"area":null},{"id":"2306.10347","title":"DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection","created":"2023-06-17","cited_by_count":103,"area":null},{"id":"2109.05257","title":"Towards a Rigorous Evaluation of Time-series Anomaly Detection","created":"2021-09-11","cited_by_count":97,"area":null},{"id":"2304.05055","title":"A Comprehensive Survey on Deep Graph Representation Learning","created":"2023-04-11","cited_by_count":72,"area":null},{"id":"2302.02173","title":"A Survey on Deep Learning based Time Series Analysis with Frequency Transformation","created":"2023-02-04","cited_by_count":40,"area":null},{"id":"2405.15273","title":"Towards a General Time Series Anomaly Detector with Adaptive Bottlenecks and Dual Adversarial Decoders","created":"2024-05-24","cited_by_count":38,"area":null},{"id":"2401.11391","title":"Interactive AI with Retrieval-Augmented Generation for Next Generation Networking","created":"2024-01-21","cited_by_count":34,"area":"rag"},{"id":"2407.09777","title":"Graph Transformers: A Survey","created":"2024-07-13","cited_by_count":32,"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":"2407.05639","title":"Deep Learning-based Anomaly Detection and Log Analysis for Computer Networks","created":"2024-07-08","cited_by_count":23,"area":null},{"id":"2504.04011","title":"Foundation Models for Time Series: A Survey","created":"2025-04-05","cited_by_count":23,"area":null},{"id":"2206.04149","title":"A Survey of Graph-based Deep Learning for Anomaly Detection in Distributed Systems","created":"2022-06-08","cited_by_count":20,"area":null},{"id":"2406.00134","title":"Anomaly Detection in Dynamic Graphs: A Comprehensive Survey","created":"2024-05-31","cited_by_count":19,"area":null},{"id":"2410.15997","title":"MultiRC: Joint Learning for Time Series Anomaly Prediction and Detection with Multi-scale Reconstructive Contrast","created":"2024-10-21","cited_by_count":19,"area":null},{"id":"2302.00058","title":"Graph Anomaly Detection in Time Series: A Survey","created":"2023-01-31","cited_by_count":18,"area":null},{"id":"2310.15416","title":"Nominality Score Conditioned Time Series Anomaly Detection by Point/Sequential Reconstruction","created":"2023-10-24","cited_by_count":17,"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":"2503.13195","title":"Deep Learning Advancements in Anomaly Detection: A Comprehensive Survey","created":"2025-03-17","cited_by_count":17,"area":null},{"id":"2203.05167","title":"TiSAT: Time Series Anomaly Transformer","created":"2022-03-10","cited_by_count":15,"area":null},{"id":"2408.14484","title":"Agentic Retrieval-Augmented Generation for Time Series Analysis","created":"2024-08-18","cited_by_count":14,"area":"rag"},{"id":"2504.20462","title":"TAMO: Fine-Grained Root Cause Analysis via Tool-Assisted LLM Agent with Multi-Modality Observation Data in Cloud-Native Systems","created":"2025-04-29","cited_by_count":13,"area":"agents"},{"id":"2108.08404","title":"Federated Variational Learning for Anomaly Detection in Multivariate Time Series","created":"2021-08-18","cited_by_count":12,"area":null},{"id":"2401.09793","title":"PatchAD: A Lightweight Patch-based MLP-Mixer for Time Series Anomaly Detection","created":"2024-01-18","cited_by_count":12,"area":null},{"id":"2501.13493","title":"GCAD: Anomaly Detection in Multivariate Time Series from the Perspective of Granger Causality","created":"2025-01-23","cited_by_count":12,"area":null},{"id":"2302.03284","title":"Unsupervised Deep Learning for IoT Time Series","created":"2023-02-07","cited_by_count":11,"area":null},{"id":"2307.03761","title":"DyEdgeGAT: Dynamic Edge via Graph Attention for Early Fault Detection in IIoT Systems","created":"2023-07-07","cited_by_count":10,"area":null},{"id":"2310.08800","title":"DDMT: Denoising Diffusion Mask Transformer Models for Multivariate Time Series Anomaly Detection","created":"2023-10-13","cited_by_count":10,"area":"diffusion"},{"id":"2310.20218","title":"A Systematic Review for Transformer-based Long-term Series Forecasting","created":"2023-10-31","cited_by_count":10,"area":null},{"id":"2401.05800","title":"Graph Spatiotemporal Process for Multivariate Time Series Anomaly Detection with Missing Values","created":"2024-01-11","cited_by_count":10,"area":null},{"id":"2403.09223","title":"MCformer: Multivariate Time Series Forecasting with Mixed-Channels Transformer","created":"2024-03-14","cited_by_count":10,"area":null},{"id":"2404.18948","title":"Sub-Adjacent Transformer: Improving Time Series Anomaly Detection with Reconstruction Error from Sub-Adjacent Neighborhoods","created":"2024-04-27","cited_by_count":10,"area":null},{"id":"2212.09518","title":"FedTADBench: Federated Time-Series Anomaly Detection Benchmark","created":"2022-12-19","cited_by_count":6,"area":null},{"id":"2308.13068","title":"Multivariate Time Series Anomaly Detection: Fancy Algorithms and Flawed Evaluation Methodology","created":"2023-08-24","cited_by_count":6,"area":null},{"id":"2312.09478","title":"Entropy Causal Graphs for Multivariate Time Series Anomaly Detection","created":"2023-12-15","cited_by_count":6,"area":null},{"id":"2408.03747","title":"Online Model-based Anomaly Detection in Multivariate Time Series: Taxonomy, Survey, Research Challenges and Future Directions","created":"2024-08-07","cited_by_count":6,"area":null},{"id":"2505.00240","title":"LLM-Based Threat Detection and Prevention Framework for IoT Ecosystems","created":"2025-05-01","cited_by_count":6,"area":null},{"id":"2303.06879","title":"Spacecraft Anomaly Detection with Attention Temporal Convolution Network","created":"2023-03-13","cited_by_count":5,"area":null},{"id":"2411.11641","title":"TSINR: Capturing Temporal Continuity via Implicit Neural Representations for Time Series Anomaly Detection","created":"2024-11-18","cited_by_count":5,"area":null},{"id":"2504.14206","title":"Decomposition-based multi-scale transformer framework for time series anomaly detection","created":"2025-04-19","cited_by_count":5,"area":null},{"id":"2201.09936","title":"Community-based anomaly detection using spectral graph filtering","created":"2022-01-24","cited_by_count":4,"area":null},{"id":"2307.10869","title":"Identifying Performance Issues in Cloud Service Systems Based on Relational-Temporal Features","created":"2023-07-20","cited_by_count":4,"area":null},{"id":"2406.15638","title":"Root Cause Analysis of Anomalies in 5G RAN Using Graph Neural Network and Transformer","created":"2024-06-21","cited_by_count":4,"area":null},{"id":"2408.14756","title":"Training-Free Time-Series Anomaly Detection: Leveraging Image Foundation Models","created":"2024-08-27","cited_by_count":4,"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":"2112.09293","title":"A Comparative Study of Detecting Anomalies in Time Series Data Using LSTM and TCN Models","created":"2021-12-17","cited_by_count":3,"area":null},{"id":"2305.17149","title":"Diagnostic Spatio-temporal Transformer with Faithful Encoding","created":"2023-05-26","cited_by_count":3,"area":null},{"id":"2306.07114","title":"Coupled Attention Networks for Multivariate Time Series Anomaly Detection","created":"2023-06-12","cited_by_count":3,"area":null},{"id":"2506.11040","title":"Large Language models for Time Series Analysis: Techniques, Applications, and Challenges","created":"2025-05-21","cited_by_count":3,"area":null},{"id":"2105.08643","title":"ASM2TV: An Adaptive Semi-Supervised Multi-Task Multi-View Learning Framework for Human Activity Recognition","created":"2021-05-18","cited_by_count":2,"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":"2204.09942","title":"Cloud-Edge Collaborative Data Anomaly Detection in Industrial Sensor Networks","created":"2022-04-21","cited_by_count":2,"area":null},{"id":"2308.00393","title":"A Survey of Time Series Anomaly Detection Methods in the AIOps Domain","created":"2023-08-01","cited_by_count":2,"area":null},{"id":"2401.05896","title":"The Role of Deep Learning in Advancing Proactive Cybersecurity Measures for Smart Grid Networks: A Survey","created":"2024-01-11","cited_by_count":2,"area":null},{"id":"2401.13872","title":"Edge Conditional Node Update Graph Neural Network for Multi-variate Time Series Anomaly Detection","created":"2024-01-25","cited_by_count":2,"area":null},{"id":"2409.13053","title":"Towards Unbiased Evaluation of Time-series Anomaly Detector","created":"2024-09-19","cited_by_count":2,"area":null},{"id":"2504.11623","title":"Possibility for Proactive Anomaly Detection","created":"2025-04-15","cited_by_count":2,"area":null},{"id":"2510.20868","title":"Crisis-Resilient Portfolio Management via Graph-based Spatio-Temporal Learning","created":"2025-10-23","cited_by_count":2,"area":null},{"id":"2511.20044","title":"RED-F: Reconstruction-Elimination based Dual-stream Contrastive Forecasting for Multivariate Time Series Anomaly Prediction","created":"2025-11-25","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":"2302.09800","title":"CNTS: Cooperative Network for Time Series","created":"2023-02-20","cited_by_count":1,"area":null},{"id":"2304.07898","title":"Harnessing Contrastive Learning and Neural Transformation for Time Series Anomaly Detection","created":"2023-04-16","cited_by_count":1,"area":null},{"id":"2312.01729","title":"EdgeConvFormer: Dynamic Graph CNN and Transformer based Anomaly Detection in Multivariate Time Series","created":"2023-12-04","cited_by_count":1,"area":null},{"id":"2502.16896","title":"Zero-shot Load Forecasting for Integrated Energy Systems: A Large Language Model-based Framework with Multi-task Learning","created":"2025-02-24","cited_by_count":1,"area":null},{"id":"2506.00188","title":"Cluster-Aware Causal Mixer for Online Anomaly Detection in Multivariate Time Series","created":"2025-05-30","cited_by_count":1,"area":null},{"id":"2506.20574","title":"Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time Series","created":"2025-06-25","cited_by_count":1,"area":null},{"id":"2509.15153","title":"AnoF-Diff: One-Step Diffusion-Based Anomaly Detection for Forceful Tool Use","created":"2025-09-18","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":"2601.23147","title":"Securing Time Integrity in Energy IoT Against Clock Drift and Y2K38 Failures","created":"2026-01-30","cited_by_count":1,"area":null},{"id":"2607.15799","title":"Knowledge-Assisted Multi-Graph Dependency Learning for Multivariate Time Series Anomaly Detection in Multi-Stage Industrial Processes","created":"2026-07-17","cited_by_count":1,"area":null},{"id":"2110.05015","title":"A Survey on Proactive Customer Care: Enabling Science and Steps to Realize it","created":"2021-10-11","cited_by_count":0,"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":"2311.15924","title":"Diagnosis driven Anomaly Detection for CPS","created":"2023-11-27","cited_by_count":0,"area":null},{"id":"2403.10220","title":"From Chaos to Clarity: Time Series Anomaly Detection in Astronomical Observations","created":"2024-03-15","cited_by_count":0,"area":null},{"id":"2403.14738","title":"A task of anomaly detection for a smart satellite Internet of things system","created":"2024-03-21","cited_by_count":0,"area":null},{"id":"2403.18451","title":"CoRAST: Towards Foundation Model-Powered Correlated Data Analysis in Resource-Constrained CPS and IoT","created":"2024-03-27","cited_by_count":0,"area":null},{"id":"2404.08224","title":"HCL-MTSAD: Hierarchical Contrastive Consistency Learning for Accurate Detection of Industrial Multivariate Time Series Anomalies","created":"2024-04-12","cited_by_count":0,"area":null},{"id":"2408.11359","title":"Hypergraph Learning based Recommender System for Anomaly Detection, Control and Optimization","created":"2024-08-21","cited_by_count":0,"area":null},{"id":"2410.21006","title":"A Review of Graph-Powered Data Quality Applications for IoT Monitoring Sensor Networks","created":"2024-10-28","cited_by_count":0,"area":null},{"id":"2411.17218","title":"GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network","created":"2024-11-26","cited_by_count":0,"area":null},{"id":"2503.00036","title":"A Novel Spatiotemporal Correlation Anomaly Detection Method Based on Time-Frequency-Domain Feature Fusion and a Dynamic Graph Neural Network in Wireless Sensor Network","created":"2025-02-25","cited_by_count":0,"area":null},{"id":"2506.00420","title":"A New Spatiotemporal Correlation Anomaly Detection Method that Integrates Contrastive Learning and Few-Shot Learning in Wireless Sensor Networks","created":"2025-05-31","cited_by_count":0,"area":null},{"id":"2506.10613","title":"Data Driven Diagnosis for Large Cyber-Physical-Systems with Minimal Prior Information","created":"2025-06-12","cited_by_count":0,"area":null},{"id":"2509.04449","title":"ChronoGraph: A Real-World Graph-Based Multivariate Time Series Dataset","created":"2025-09-04","cited_by_count":0,"area":null},{"id":"2509.05663","title":"DQS: A Low-Budget Query Strategy for Enhancing Unsupervised Data-driven Anomaly Detection Approaches","created":"2025-09-06","cited_by_count":0,"area":null},{"id":"2509.11601","title":"Dynamic Adaptive Parsing of Temporal and Cross-Variable Patterns for Network State Classification","created":"2025-09-15","cited_by_count":0,"area":null},{"id":"2509.17235","title":"Prospective Multi-Graph Cohesion for Multivariate Time Series Anomaly Detection","created":"2025-09-21","cited_by_count":0,"area":null},{"id":"2510.11141","title":"A Comprehensive Forecasting-Based Framework for Time Series Anomaly Detection: Benchmarking on the Numenta Anomaly Benchmark (NAB)","created":"2025-10-13","cited_by_count":0,"area":null},{"id":"2511.23000","title":"A Modular Framework for Rapidly Building Intrusion Predictors","created":"2025-11-28","cited_by_count":0,"area":null},{"id":"2601.00446","title":"A Comparative Study of Adaptation Strategies for Time Series Foundation Models in Anomaly Detection","created":"2026-01-01","cited_by_count":0,"area":null},{"id":"2602.11539","title":"Real-Time Proactive Anomaly Detection via Forward and Backward Forecast Modeling","created":"2026-02-12","cited_by_count":0,"area":null},{"id":"2602.15457","title":"Benchmarking IoT Time-Series AD with Event-Level Augmentations","created":"2026-02-17","cited_by_count":0,"area":null},{"id":"2603.09675","title":"GNNs for Time Series Anomaly Detection: An Open-Source Framework and a Critical Evaluation","created":"2026-03-10","cited_by_count":0,"area":null},{"id":"2603.18941","title":"Unified Taxonomy for Multivariate Time Series Anomaly Detection using Deep Learning","created":"2026-03-19","cited_by_count":0,"area":null},{"id":"2603.18985","title":"Revisiting OmniAnomaly for Anomaly Detection: performance metrics and comparison with PCA-based models","created":"2026-03-19","cited_by_count":0,"area":null},{"id":"2605.00929","title":"PhaseNet++: Phase-Aware Frequency-Domain Anomaly Detection for Industrial Control Systems via Phase Coherence Graphs","created":"2026-04-30","cited_by_count":0,"area":null},{"id":"2605.25135","title":"ASTRO: Adaptive Spatio-Temporal Reinforcement Optimization for GNN Powered Anomly Detection in Cyber Physical Systems","created":"2026-05-24","cited_by_count":0,"area":null},{"id":"2606.01691","title":"IstGPT: LLM-based Anomaly Detection for Spatial-Temporal Graph in Industrial Systems","created":"2026-06-01","cited_by_count":0,"area":null}],"next_cursor":"100"}