mlsanityData to 7 Oct 2026

Time-Series Anomaly Detection Service at Microsoft

arXiv 1906.03821 ↗First posted 10 Jun 2019cs.LG stat.MLcited by 163

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Abstract (arXiv)

Large companies need to monitor various metrics (for example, Page Views and Revenue) of their applications and services in real time. At Microsoft, we develop a time-series anomaly detection service which helps customers to monitor the time-series continuously and alert for potential incidents on time. In this paper, we introduce the pipeline and algorithm of our anomaly detection service, which is designed to be accurate, efficient and general. The pipeline consists of three major modules, including data ingestion, experimentation platform and online compute. To tackle the problem of time-series anomaly detection, we propose a novel algorithm based on Spectral Residual (SR) and Convolutional Neural Network (CNN). Our work is the first attempt to borrow the SR model from visual saliency detection domain to time-series anomaly detection. Moreover, we innovatively combine SR and CNN together to improve the performance of SR model. Our approach achieves superior experimental results compared with state-of-the-art baselines on both public datasets and Microsoft production data.

Key facts. Cited by 163 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Jun 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 19; 2022: 25; 2023: 38; 2024: 24; 2025: 26; 2026: 31.

163 papers.

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  1. 2021TS2Vec: Towards Universal Representation of Time Seriescited by 463
  2. 2021Spectral Temporal Graph Neural Network for Multivariate Time-series Forecastingcited by 298
  3. 2022Deep Learning for Time Series Anomaly Detection: A Surveycited by 130
  4. 2023DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detectioncited by 103
  5. 2024CATCH: Channel-Aware multivariate Time Series Anomaly Detection via Frequency Patchingcited by 91
  6. 2022Local Evaluation of Time Series Anomaly Detection Algorithmscited by 79
  7. 2021An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Seriescited by 68
  8. 2025TAB: Unified Benchmarking of Time Series Anomaly Detection Methodscited by 58
  9. 2023A Survey on Time-Series Pre-Trained Modelscited by 52
  10. 2023TimesURL: Self-supervised Contrastive Learning for Universal Time Series Representation Learningcited by 51
  11. 2023Soft Contrastive Learning for Time Seriescited by 49
  12. 2022TFAD: A Decomposition Time Series Anomaly Detection Architecture with Time-Frequency Analysiscited by 48
38 more
  1. 2021Neural Contextual Anomaly Detection for Time Seriescited by 42
  2. 2023A Survey on Deep Learning based Time Series Analysis with Frequency Transformationcited by 40
  3. 2024Towards a General Time Series Anomaly Detector with Adaptive Bottlenecks and Dual Adversarial Decoderscited by 37
  4. 2024TimeDiT: General-purpose Diffusion Transformers for Time Series Foundation Modelcited by 33
  5. 2024Heterogeneous Contrastive Learning for Foundation Models and Beyondcited by 27
  6. 2024Large Language Models can Deliver Accurate and Interpretable Time Series Anomaly Detectioncited by 26
  7. 2021Merlion: A Machine Learning Library for Time Seriescited by 25
  8. 2023Navigating the Metric Maze: A Taxonomy of Evaluation Metrics for Anomaly Detection in Time Seriescited by 24
  9. 2023Xpert: Empowering Incident Management with Query Recommendations via Large Language Modelscited by 22
  10. 2024Revisiting VAE for Unsupervised Time Series Anomaly Detection: A Frequency Perspectivecited by 22
  11. 2024TimeSeriesBench: An Industrial-Grade Benchmark for Time Series Anomaly Detection Modelscited by 22
  12. 2023MST-GAT: A Multimodal Spatial-Temporal Graph Attention Network for Time Series Anomaly Detectioncited by 20
  13. 2024KAN-AD: Time Series Anomaly Detection with Kolmogorov-Arnold Networkscited by 20
  14. 2023T-Rep: Representation Learning for Time Series using Time-Embeddingscited by 19
  15. 2024Graph Mixture of Experts and Memory-augmented Routers for Multivariate Time Series Anomaly Detectioncited by 19
  16. 2023Graph Anomaly Detection in Time Series: A Surveycited by 18
  17. 2023Correlation-aware Spatial-Temporal Graph Learning for Multivariate Time-series Anomaly Detectioncited by 17
  18. 2022Sintel: A Machine Learning Framework to Extract Insights from Signalscited by 16
  19. 2022TiSAT: Time Series Anomaly Transformercited by 15
  20. 2022FECAM: Frequency Enhanced Channel Attention Mechanism for Time Series Forecastingcited by 15
  21. 2023Explainable Time Series Anomaly Detection using Masked Latent Generative Modelingcited by 15
  22. 2021RLAD: Time Series Anomaly Detection through Reinforcement Learning and Active Learningcited by 13
  23. 2021Online false discovery rate control for anomaly detection in time seriescited by 13
  24. 2023AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challengescited by 13
  25. 2021Federated Variational Learning for Anomaly Detection in Multivariate Time Seriescited by 12
  26. 2023Unsupervised Deep Learning for IoT Time Seriescited by 11
  27. 2022Deep Contrastive One-Class Time Series Anomaly Detectioncited by 10
  28. 2023A Multi-Scale Decomposition MLP-Mixer for Time Series Analysiscited by 10
  29. 2024Continual Learning for Smart City: A Surveycited by 9
  30. 2025Towards Foundation Models for Zero-Shot Time Series Anomaly Detection: Leveraging Synthetic Data and Relative Context Discrepancycited by 9
  31. 2021How Far Should We Look Back to Achieve Effective Real-Time Time-Series Anomaly Detection?cited by 8
  32. 2021SALAD: Self-Adaptive Lightweight Anomaly Detection for Real-time Recurrent Time Seriescited by 8
  33. 2022A Causal Approach to Detecting Multivariate Time-series Anomalies and Root Causescited by 8
  34. 2022AER: Auto-Encoder with Regression for Time Series Anomaly Detectioncited by 8
  35. 2023Heterogeneous Anomaly Detection for Software Systems via Semi-supervised Cross-modal Attentioncited by 8
  36. 2024PQMass: Probabilistic Assessment of the Quality of Generative Models using Probability Mass Estimationcited by 8
  37. 2025Multivariate Time Series Anomaly Detection by Capturing Coarse-Grained Intra- and Inter-Variate Dependenciescited by 8
  38. 2025Time-RA: Towards Time Series Reasoning for Anomaly Diagnosis with LLM Feedbackcited by 7

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