mlsanityData to 14 Oct 2026

TAB: Unified Benchmarking of Time Series Anomaly Detection Methods

arXiv 2506.18046 ↗First posted 22 Jun 2025cs.LGcited by 60

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Abstract (arXiv)

Time series anomaly detection (TSAD) plays an important role in many domains such as finance, transportation, and healthcare. With the ongoing instrumentation of reality, more time series data will be available, leading also to growing demands for TSAD. While many TSAD methods already exist, new and better methods are still desirable. However, effective progress hinges on the availability of reliable means of evaluating new methods and comparing them with existing methods. We address deficiencies in current evaluation procedures related to datasets and experimental settings and protocols. Specifically, we propose a new time series anomaly detection benchmark, called TAB. First, TAB encompasses 29 public multivariate datasets and 1,635 univariate time series from different domains to facilitate more comprehensive evaluations on diverse datasets. Second, TAB covers a variety of TSAD methods, including Non-learning, Machine learning, Deep learning, LLM-based, and Time-series pre-trained methods. Third, TAB features a unified and automated evaluation pipeline that enables fair and easy evaluation of TSAD methods. Finally, we employ TAB to evaluate existing TSAD methods and report on the outcomes, thereby offering a deeper insight into the performance of these methods. Besides, all datasets and code are available at https://github.com/decisionintelligence/TAB.

Key facts. Cited by 60 papers in the map (data through 14 Oct 2026). Builds on 41 of its 123 references that are in the map. First posted on arXiv 22 Jun 2025.

Builds on

41 of 123 references.

References · 41

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 2,039
  3. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,424
  4. 2024Chronos: Learning the Language of Time Seriescited by 759
  5. 2023One Fits All:Power General Time Series Analysis by Pretrained LMcited by 632
  6. 2024MOMENT: A Family of Open Time-series Foundation Modelscited by 482
  7. 2018The UCR Time Series Archivecited by 431
  8. 2018Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholdingcited by 410
  9. 2021Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancycited by 394
  10. 2024TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methodscited by 249
  11. 2018Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applicationscited by 224
  12. 2019Time-Series Anomaly Detection Service at Microsoftcited by 165
29 more
  1. 2023UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecastingcited by 144
  2. 2022Deep Learning for Time Series Anomaly Detection: A Surveycited by 131
  3. 2023Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysiscited by 117
  4. 2024CALF: Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuningcited by 108
  5. 2021Towards a Rigorous Evaluation of Time-series Anomaly Detectioncited by 97
  6. 2015Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly Benchmarkcited by 88
  7. 2022Local Evaluation of Time Series Anomaly Detection Algorithmscited by 79
  8. 2024FreDF: Learning to Forecast in the Frequency Domaincited by 79
  9. 2018Precision and Recall for Time Seriescited by 67
  10. 2024TSFM-Bench: A Comprehensive and Unified Benchmark of Foundation Models for Time Series Forecastingcited by 58
  11. 2025A Comprehensive Survey of Deep Learning for Multivariate Time Series Forecasting: A Channel Strategy Perspectivecited by 58
  12. 2020Exathlon: A Benchmark for Explainable Anomaly Detection over Time Seriescited by 56
  13. 2025Rethinking Irregular Time Series Forecasting: A Simple yet Effective Baselinecited by 55
  14. 2023LightTS: Lightweight Time Series Classification with Adaptive Ensemble Distillation -- Extended Versioncited by 50
  15. 2023DSformer: A Double Sampling Transformer for Multivariate Time Series Long-term Predictioncited by 49
  16. 2025FineCIR: Explicit Parsing of Fine-Grained Modification Semantics for Composed Image Retrievalcited by 42
  17. 2020TODS: An Automated Time Series Outlier Detection Systemcited by 41
  18. 2024Towards a General Time Series Anomaly Detector with Adaptive Bottlenecks and Dual Adversarial Decoderscited by 38
  19. 2016Multivariate Industrial Time Series with Cyber-Attack Simulation: Fault Detection Using an LSTM-based Predictive Data Modelcited by 34
  20. 2021Time Series Anomaly Detection for Cyber-Physical Systems via Neural System Identification and Bayesian Filteringcited by 31
  21. 2021Merlion: A Machine Learning Library for Time Seriescited by 25
  22. 2024Distilling Semantic Priors from SAM to Efficient Image Restoration Modelscited by 25
  23. 2024TimeSeriesBench: An Industrial-Grade Benchmark for Time Series Anomaly Detection Modelscited by 23
  24. 2025Merlin: Multi-View Representation Learning for Robust Multivariate Time Series Forecasting with Unfixed Missing Ratescited by 20
  25. 2024MultiRC: Joint Learning for Time Series Anomaly Prediction and Detection with Multi-scale Reconstructive Contrastcited by 19
  26. 2024ICH-SCNet: Intracerebral Hemorrhage Segmentation and Prognosis Classification Network Using CLIP-guided SAM mechanismcited by 16
  27. 2022Series2Graph: Graph-based Subsequence Anomaly Detection for Time Seriescited by 13
  28. 2022Entire Space Counterfactual Learning for Reliable Content Recommendationscited by 13
  29. 2019Adaptive Anomaly Detection in Chaotic Time Series with a Spatially Aware Echo State Networkcited by 6

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 17; 2026: 43.

60 papers.

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  1. 2025DBLoss: Decomposition-based Loss Function for Time Series Forecastingcited by 45
  2. 2025OFFSET: Segmentation-based Focus Shift Revision for Composed Image Retrievalcited by 40
  3. 2025Enhancing Time Series Forecasting through Selective Representation Spaces: A Patch Perspectivecited by 39
  4. 2025Aurora: Towards Universal Generative Multimodal Time Series Forecastingcited by 29
  5. 2026FastVMT: Eliminating Redundancy in Video Motion Transfercited by 23
  6. 2025Bridging Past and Future: Distribution-Aware Alignment for Time Series Forecastingcited by 20
  7. 2026GCGNet: Graph-Consistent Generative Network for Time Series Forecasting with Exogenous Variablescited by 13
  8. 2025CRISP-SAM2: SAM2 with Cross-Modal Interaction and Semantic Prompting for Multi-Organ Segmentationcited by 12
  9. 2025CrossAD: Time Series Anomaly Detection with Cross-scale Associations and Cross-window Modelingcited by 12
  10. 2026Bridging Time and Frequency: A Joint Modeling Framework for Irregular Multivariate Time Series Forecastingcited by 9
  11. 2025ASTGI: Adaptive Spatio-Temporal Graph Interactions for Irregular Multivariate Time Series Forecastingcited by 8
  12. 2025STAR: Boosting Time Series Foundation Models for Anomaly Detection through State-aware Adaptercited by 7
38 more
  1. 2026AnomaMind: Agentic Time Series Anomaly Detection with Tool-Augmented Reasoningcited by 5
  2. 2026CLIR-Bench: Benchmarking Multimodal Question Answering over Irregular Clinical Time Seriescited by 4
  3. 2025Diffusion-Based Image Editing: An Unforeseen Adversary to Robust Invisible Watermarkscited by 3
  4. 2025FLAME: Flow Enhanced Legendre Memory Models for General Time Series Forecastingcited by 3
  5. 2026PHAT: Modeling Period Heterogeneity for Multivariate Time Series Forecastingcited by 3
  6. 2026Time Series Reasoning via Process-Verifiable Thinking Data Synthesis and Scheduling for Tailored LLM Reasoningcited by 3
  7. 2026Towards Multimodal Time Series Anomaly Detection with Semantic Alignment and Condensed Interactioncited by 3
  8. 2025Task-Aware Mixture-of-Experts for Time Series Analysiscited by 2
  9. 2026TimeMar: Multi-Scale Autoregressive Modeling for Unconditional Time Series Generationcited by 2
  10. 2026MEMTS: Internalizing Domain Knowledge via Parameterized Memory for Retrieval-Free Domain Adaptation of Time Series Foundation Modelscited by 2
  11. 2026Vanishing Watermarks: Diffusion-Based Image Editing Undermines Robust Invisible Watermarkingcited by 2
  12. 2026WPBench: A Comprehensive Benchmark for Wind Power Forecastingcited by 2
  13. 2025Leveraging Intermediate Representations of Time Series Foundation Models for Anomaly Detectioncited by 1
  14. 2026SEER: Transformer-based Robust Time Series Forecasting via Automated Patch Enhancement and Replacementcited by 1
  15. 2026ST-EVO: Towards Generative Spatio-Temporal Evolution of Multi-Agent Communication Topologiescited by 1
  16. 2026RAMSeS: Robust and Adaptive Model Selection for Time-Series Anomaly Detection Algorithmscited by 1
  17. 2026FCL-COD: Weakly Supervised Camouflaged Object Detection with Frequency-aware and Contrastive Learningcited by 1
  18. 2026Editing on the Generative Manifold: A Theoretical and Empirical Study of General Diffusion-Based Image Editing Trade-offscited by 1
  19. 2026ASTER: Latent Pseudo-Anomaly Generation for Unsupervised Time-Series Anomaly Detectioncited by 1
  20. 2026L-Drive: Beyond a Single Mapping-Latent Context Drives Time Series Forecastingcited by 1
  21. 2026Disjoint or Overlapping? Inference Windowing for Reconstruction-Based Time Series Anomaly Detectioncited by 1
  22. 2026QiYao-M: Multimodal Time Series Foundation Model with Role-Aware Modeling of Endogenous and Exogenous Modalitiescited by 1
  23. 2025FADE: Adversarial Concept Erasure in Flow Modelscited by 0
  24. 2025A Multimodal Deep Learning Framework for Early Diagnosis of Liver Cancer via Optimized BiLSTM-AM-VMD Architecturecited by 0
  25. 2025Robust Concept Erasure in Diffusion Models: A Theoretical Perspective on Security and Robustnesscited by 0
  26. 2025SimDiff: Simpler Yet Better Diffusion Model for Time Series Point Forecastingcited by 0
  27. 2026LEFT: Learnable Fusion of Tri-view Tokens for Unsupervised Time Series Anomaly Detectioncited by 0
  28. 2026AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detectioncited by 0
  29. 2026CGSTA: Cross-Scale Graph Contrast with Stability-Aware Alignment for Multivariate Time-Series Anomaly Detectioncited by 0
  30. 2026When Denoising Becomes Unsigning: Theoretical and Empirical Analysis of Watermark Fragility Under Diffusion-Based Image Editingcited by 0
  31. 2026ECoLAD: Selecting Anomaly Detectors for Automotive Deployment via Compute-Reduction Evaluationcited by 0
  32. 2026Editing Away the Evidence: Diffusion-Based Image Manipulation and the Failure Modes of Robust Watermarkingcited by 0
  33. 2026VAN-AD: Visual Masked Autoencoder with Normalizing Flow For Time Series Anomaly Detectioncited by 0
  34. 2026CombinationTS: A Modular Framework for Understanding Time-Series Forecasting Modelscited by 0
  35. 2026VLBM: Variational Latent Basis Modeling for OOD Robust Multivariate Time Series Forecastingcited by 0
  36. 2026TPA-AD: A Two-Stage Pseudo Anomaly-Guided Method for Bearing Time-Series Anomaly Detectioncited by 0
  37. 2026On the Controllability-Fidelity Frontier in Diffusion Editingcited by 0
  38. 2026CAAD: Causality-Aware Multivariate Time Series Anomaly Detection via Multi-Scale Alignment and Structural Causal Consistencycited by 0

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