{"total":151,"rows":[{"id":"2402.03885","title":"MOMENT: A Family of Open Time-series Foundation Models","created":"2024-02-06","cited_by_count":482,"area":null},{"id":"2201.07284","title":"TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data","created":"2022-01-18","cited_by_count":214,"area":null},{"id":"2402.02368","title":"Timer: Generative Pre-trained Transformers Are Large Time Series Models","created":"2024-02-04","cited_by_count":208,"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":"2109.05257","title":"Towards a Rigorous Evaluation of Time-series Anomaly Detection","created":"2021-09-11","cited_by_count":97,"area":null},{"id":"2206.06602","title":"Deep Isolation Forest for Anomaly Detection","created":"2022-06-14","cited_by_count":92,"area":null},{"id":"2206.13167","title":"Local Evaluation of Time Series Anomaly Detection Algorithms","created":"2022-06-27","cited_by_count":79,"area":null},{"id":"2109.11428","title":"An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Series","created":"2021-09-23","cited_by_count":68,"area":null},{"id":"2305.10716","title":"A Survey on Time-Series Pre-Trained Models","created":"2023-05-18","cited_by_count":52,"area":null},{"id":"2107.07702","title":"Neural Contextual Anomaly Detection for Time Series","created":"2021-07-16","cited_by_count":43,"area":null},{"id":"2410.05440","title":"Can LLMs Understand Time Series Anomalies?","created":"2024-10-07","cited_by_count":41,"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":"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":"2401.03717","title":"Universal Time-Series Representation Learning: A Survey","created":"2024-01-08","cited_by_count":30,"area":null},{"id":"2401.15123","title":"Large Language Model Guided Knowledge Distillation for Time Series Anomaly Detection","created":"2024-01-26","cited_by_count":26,"area":"distill"},{"id":"2210.01078","title":"Unsupervised Model Selection for Time-series Anomaly Detection","created":"2022-10-03","cited_by_count":25,"area":null},{"id":"2502.15637","title":"Mantis: Lightweight Foundation Model for Time Series Classification","created":"2025-02-21","cited_by_count":24,"area":null},{"id":"2402.10802","title":"TimeSeriesBench: An Industrial-Grade Benchmark for Time Series Anomaly Detection Models","created":"2024-02-16","cited_by_count":23,"area":null},{"id":"2411.00278","title":"KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networks","created":"2024-11-01","cited_by_count":23,"area":null},{"id":"2208.02108","title":"Detecting Multivariate Time Series Anomalies with Zero Known Label","created":"2022-08-03","cited_by_count":21,"area":null},{"id":"2204.01637","title":"Do Deep Neural Networks Contribute to Multivariate Time Series Anomaly Detection?","created":"2022-04-04","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":"2307.08390","title":"Correlation-aware Spatial-Temporal Graph Learning for Multivariate Time-series Anomaly Detection","created":"2023-07-17","cited_by_count":18,"area":null},{"id":"2202.07586","title":"Deep Generative model with Hierarchical Latent Factors for Time Series Anomaly Detection","created":"2022-02-15","cited_by_count":16,"area":null},{"id":"2304.04661","title":"AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges","created":"2023-04-10","cited_by_count":15,"area":null},{"id":"2311.12550","title":"Explainable Time Series Anomaly Detection using Masked Latent Generative Modeling","created":"2023-11-21","cited_by_count":15,"area":null},{"id":"2312.11549","title":"Label-Free Multivariate Time Series Anomaly Detection","created":"2023-12-17","cited_by_count":14,"area":null},{"id":"2112.03196","title":"Online false discovery rate control for anomaly detection in time series","created":"2021-12-06","cited_by_count":13,"area":null},{"id":"2301.12495","title":"Time-Series Pattern Recognition in Smart Manufacturing Systems: A Literature Review and Ontology","created":"2023-01-29","cited_by_count":13,"area":null},{"id":"2202.03944","title":"Detecting Anomalies within Time Series using Local Neural Transformations","created":"2022-02-08","cited_by_count":12,"area":null},{"id":"2311.11235","title":"Unraveling the \"Anomaly\" in Time Series Anomaly Detection: A Self-supervised Tri-domain Solution","created":"2023-11-19","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":"2409.03024","title":"NUMOSIM: A Synthetic Mobility Dataset with Anomaly Detection Benchmarks","created":"2024-09-04","cited_by_count":12,"area":null},{"id":"2409.18874","title":"CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly Detection and Forecasting","created":"2024-09-27","cited_by_count":12,"area":null},{"id":"2510.12489","title":"CrossAD: Time Series Anomaly Detection with Cross-scale Associations and Cross-window Modeling","created":"2025-10-14","cited_by_count":12,"area":null},{"id":"2602.17868","title":"MantisV2: Closing the Zero-Shot Gap in Time Series Classification with Synthetic Data and Test-Time Strategies","created":"2026-02-19","cited_by_count":11,"area":null},{"id":"2207.01472","title":"Deep Contrastive One-Class Time Series Anomaly Detection","created":"2022-07-04","cited_by_count":10,"area":null},{"id":"2112.12965","title":"Error-bounded Approximate Time Series Joins Using Compact Dictionary Representations of Time Series","created":"2021-12-24","cited_by_count":9,"area":null},{"id":"2212.11080","title":"Is it worth it? Comparing six deep and classical methods for unsupervised anomaly detection in time series","created":"2022-12-21","cited_by_count":9,"area":null},{"id":"2212.13558","title":"AER: Auto-Encoder with Regression for Time Series Anomaly Detection","created":"2022-12-27","cited_by_count":8,"area":null},{"id":"2310.10461","title":"Model Selection of Anomaly Detectors in the Absence of Labeled Validation Data","created":"2023-10-16","cited_by_count":8,"area":null},{"id":"2502.14045","title":"There are no Champions in Supervised Long-Term Time Series Forecasting","created":"2025-02-19","cited_by_count":8,"area":null},{"id":"2507.15066","title":"Time-RA: Towards Time Series Reasoning for Anomaly Diagnosis with LLM Feedback","created":"2025-07-20","cited_by_count":8,"area":null},{"id":"2407.04730","title":"The OPS-SAT benchmark for detecting anomalies in satellite telemetry","created":"2024-06-29","cited_by_count":7,"area":null},{"id":"2506.21550","title":"mTSBench: Benchmarking Multivariate Time Series Anomaly Detection and Model Selection at Scale","created":"2025-06-26","cited_by_count":7,"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":"2405.11238","title":"SimAD: A Simple Dissimilarity-based Approach for Time Series Anomaly Detection","created":"2024-05-18","cited_by_count":6,"area":null},{"id":"2407.15247","title":"TimeInf: Time Series Data Contribution via Influence Functions","created":"2024-07-21","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":"2410.04154","title":"Applying Quantum Autoencoders for Time Series Anomaly Detection","created":"2024-10-05","cited_by_count":6,"area":null},{"id":"2506.08641","title":"Unlocking Pretrained Vision Transformers for Time Series Classification","created":"2025-06-10","cited_by_count":6,"area":null},{"id":"2604.21199","title":"ARFBench: Benchmarking Time Series Question Answering Ability for Software Incident Response","created":"2026-04-23","cited_by_count":6,"area":null},{"id":"2209.05899","title":"A Meta-level Analysis of Online Anomaly Detectors","created":"2022-09-13","cited_by_count":5,"area":null},{"id":"2510.06063","title":"TelecomTS: A Multi-Modal Observability Dataset for Time Series and Language Analysis","created":"2025-10-07","cited_by_count":5,"area":null},{"id":"2510.07513","title":"MLLM4TS: Leveraging Vision and Multimodal Language Models for General Time-Series Analysis","created":"2025-10-08","cited_by_count":5,"area":"mllm"},{"id":"2212.14630","title":"Detecting Change Intervals with Isolation Distributional Kernel","created":"2022-12-30","cited_by_count":4,"area":null},{"id":"2310.12294","title":"Open-Set Multivariate Time-Series Anomaly Detection","created":"2023-10-18","cited_by_count":4,"area":null},{"id":"2501.15196","title":"A Review on Self-Supervised Learning for Time Series Anomaly Detection: Recent Advances and Open Challenges","created":"2025-01-25","cited_by_count":4,"area":null},{"id":"2502.08262","title":"GenIAS: Generator for Instantiating Anomalies in time Series","created":"2025-02-12","cited_by_count":4,"area":null},{"id":"2503.18385","title":"RoCA: Robust Contrastive One-class Time Series Anomaly Detection with Contaminated Data","created":"2025-03-24","cited_by_count":4,"area":null},{"id":"2510.16511","title":"Structured Temporal Causality for Interpretable Multivariate Time Series Anomaly Detection","created":"2025-10-18","cited_by_count":4,"area":null},{"id":"2303.12952","title":"TSI-GAN: Unsupervised Time Series Anomaly Detection using Convolutional Cycle-Consistent Generative Adversarial Networks","created":"2023-03-22","cited_by_count":3,"area":null},{"id":"2308.12563","title":"Contaminated Multivariate Time-Series Anomaly Detection with Spatio-Temporal Graph Conditional Diffusion Models","created":"2023-08-24","cited_by_count":3,"area":"diffusion"},{"id":"2311.03393","title":"Sketching Multidimensional Time Series for Fast Discord Mining","created":"2023-11-05","cited_by_count":3,"area":null},{"id":"2311.10421","title":"Is Your Anomaly Detector Ready for Change? Adapting AIOps Solutions to the Real World","created":"2023-11-17","cited_by_count":3,"area":null},{"id":"2401.09489","title":"PUPAE: Intuitive and Actionable Explanations for Time Series Anomalies","created":"2024-01-16","cited_by_count":3,"area":null},{"id":"2405.03234","title":"A Reliable Framework for Human-in-the-Loop Anomaly Detection in Time Series","created":"2024-05-06","cited_by_count":3,"area":null},{"id":"2505.07750","title":"The Pitfalls of Benchmarking in Algorithm Selection: What We Are Getting Wrong","created":"2025-05-12","cited_by_count":3,"area":null},{"id":"2606.02670","title":"Anomalies in Multivariate Time Series Benchmarks Are Mostly Univariate","created":"2026-06-01","cited_by_count":3,"area":null},{"id":"2202.03140","title":"OPP-Miner: Order-preserving sequential pattern mining","created":"2022-01-09","cited_by_count":2,"area":null},{"id":"2206.14342","title":"Intrinsic Anomaly Detection for Multi-Variate Time Series","created":"2022-06-29","cited_by_count":2,"area":null},{"id":"2306.16914","title":"Computationally Assisted Quality Control for Public Health Data Streams","created":"2023-06-29","cited_by_count":2,"area":null},{"id":"2405.19823","title":"Joint Selective State Space Model and Detrending for Robust Time Series Anomaly Detection","created":"2024-05-30","cited_by_count":2,"area":null},{"id":"2407.06849","title":"TeVAE: A Variational Autoencoder Approach for Discrete Online Anomaly Detection in Variable-state Multivariate Time-series Data","created":"2024-07-09","cited_by_count":2,"area":null},{"id":"2408.03399","title":"RHiOTS: A Framework for Evaluating Hierarchical Time Series Forecasting Algorithms","created":"2024-08-06","cited_by_count":2,"area":null},{"id":"2408.06620","title":"Unveiling the Flaws: A Critical Analysis of Initialization Effect on Time Series Anomaly Detection","created":"2024-08-13","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":"2503.17410","title":"Comparative Analysis of Deep Learning Models for Real-World ISP Network Traffic Forecasting","created":"2025-03-20","cited_by_count":2,"area":null},{"id":"2503.21833","title":"Refining Time Series Anomaly Detectors using Large Language Models","created":"2025-03-26","cited_by_count":2,"area":null},{"id":"2506.02081","title":"RATFM: Retrieval-augmented Time Series Foundation Model for Anomaly Detection","created":"2025-06-02","cited_by_count":2,"area":"rag"},{"id":"2510.26643","title":"MSAD: A Deep Dive into Model Selection for Time series Anomaly Detection","created":"2025-10-30","cited_by_count":2,"area":null},{"id":"2607.01918","title":"Zeus: Towards Tuning-Free Foundation Model for Time Series Analysis","created":"2026-07-02","cited_by_count":2,"area":null},{"id":"2211.02146","title":"Robust Time Series Chain Discovery with Incremental Nearest Neighbors","created":"2022-11-03","cited_by_count":1,"area":null},{"id":"2309.11427","title":"Generative Pre-Training of Time-Series Data for Unsupervised Fault Detection in Semiconductor Manufacturing","created":"2023-09-20","cited_by_count":1,"area":null},{"id":"2310.00268","title":"Unravel Anomalies: An End-to-end Seasonal-Trend Decomposition Approach for Time Series Anomaly Detection","created":"2023-09-30","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":"2401.01459","title":"Outlier Ranking in Large-Scale Public Health Streams","created":"2024-01-02","cited_by_count":1,"area":null},{"id":"2404.10320","title":"CARE to Compare: A real-world dataset for anomaly detection in wind turbine data","created":"2024-04-16","cited_by_count":1,"area":null},{"id":"2405.03075","title":"AnoGAN for Tabular Data: A Novel Approach to Anomaly Detection","created":"2024-05-05","cited_by_count":1,"area":null},{"id":"2405.07749","title":"DeepHYDRA: Resource-Efficient Time-Series Anomaly Detection in Dynamically-Configured Systems","created":"2024-05-13","cited_by_count":1,"area":null},{"id":"2408.14763","title":"Channel Matters: Estimating Channel Influence for Multivariate Time Series","created":"2024-08-27","cited_by_count":1,"area":null},{"id":"2409.09742","title":"OML-AD: Online Machine Learning for Anomaly Detection in Time Series Data","created":"2024-09-15","cited_by_count":1,"area":null},{"id":"2411.13951","title":"PATH: A Discrete-sequence Dataset for Evaluating Online Unsupervised Anomaly Detection Approaches for Multivariate Time Series","created":"2024-11-21","cited_by_count":1,"area":null},{"id":"2501.07999","title":"Unsupervised Feature Construction for Anomaly Detection in Time Series -- An Evaluation","created":"2025-01-14","cited_by_count":1,"area":null},{"id":"2503.07682","title":"A Time Series Multitask Framework Integrating a Large Language Model, Pre-Trained Time Series Model, and Knowledge Graph","created":"2025-03-10","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":"2509.12650","title":"Leveraging Intermediate Representations of Time Series Foundation Models for Anomaly Detection","created":"2025-09-16","cited_by_count":1,"area":null},{"id":"2510.01970","title":"Moon: A Modality Conversion-based Efficient Multivariate Time Series Anomaly Detection","created":"2025-10-02","cited_by_count":1,"area":null},{"id":"2511.06894","title":"COGNOS: Universal Enhancement for Time Series Anomaly Detection via Constrained Gaussian-Noise Optimization and Smoothing","created":"2025-11-10","cited_by_count":1,"area":null},{"id":"2602.13288","title":"Benchmarking Anomaly Detection Across Heterogeneous Cloud Telemetry Datasets","created":"2026-02-07","cited_by_count":1,"area":null}],"next_cursor":"100"}