mlsanityData to 14 Oct 2026

A Survey of Time Series Anomaly Detection Methods in the AIOps Domain

arXiv 2308.00393 ↗First posted 1 Aug 2023cs.LG eess.SPcited by 2

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Abstract (arXiv)

Internet-based services have seen remarkable success, generating vast amounts of monitored key performance indicators (KPIs) as univariate or multivariate time series. Monitoring and analyzing these time series are crucial for researchers, service operators, and on-call engineers to detect outliers or anomalies indicating service failures or significant events. Numerous advanced anomaly detection methods have emerged to address availability and performance issues. This review offers a comprehensive overview of time series anomaly detection in Artificial Intelligence for IT operations (AIOps), which uses AI capabilities to automate and optimize operational workflows. Additionally, it explores future directions for real-world and next-generation time-series anomaly detection based on recent advancements.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 17 of its 98 references that are in the map. First posted on arXiv 1 Aug 2023.

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17 of 98 references.

References · 17

  1. 2021Graph Neural Network-Based Anomaly Detection in Multivariate Time Seriescited by 301
  2. 2018Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applicationscited by 224
  3. 2020A review on outlier/anomaly detection in time series datacited by 189
  4. 2018A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Datacited by 182
  5. 2019MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networkscited by 176
  6. 2020Multivariate Time-series Anomaly Detection via Graph Attention Networkcited by 167
  7. 2019Time-Series Anomaly Detection Service at Microsoftcited by 165
  8. 2021Learning Graph Structures with Transformer for Multivariate Time Series Anomaly Detection in IoTcited by 104
  9. 2021An Evaluation of Anomaly Detection and Diagnosis in Multivariate Time Seriescited by 68
  10. 2018Precision and Recall for Time Seriescited by 67
  11. 2020Time Series Change Point Detection with Self-Supervised Contrastive Predictive Codingcited by 58
  12. 2020TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networkscited by 55
5 more
  1. 2022TFAD: A Decomposition Time Series Anomaly Detection Architecture with Time-Frequency Analysiscited by 48
  2. 2021Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles -- Extended Versioncited by 35
  3. 2022Adaptive Performance Anomaly Detection for Online Service Systems via Pattern Sketchingcited by 13
  4. 2015Contrastive Pessimistic Likelihood Estimation for Semi-Supervised Classificationcited by 6
  5. 2021StackVAE-G: An efficient and interpretable model for time series anomaly detectioncited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 1.

2 papers.

  1. 2024ChatTS: Aligning Time Series with LLMs via Synthetic Data for Enhanced Understanding and Reasoningcited by 103
  2. 2026KairosVL: Orchestrating Time Series and Semantics for Unified Reasoningcited by 0