mlsanityData to 7 Oct 2026

Dive into Time-Series Anomaly Detection: A Decade Review

arXiv 2412.20512 ↗First posted 29 Dec 2024cs.LG cs.AI cs.DBcited by 23

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Abstract (arXiv)

Recent advances in data collection technology, accompanied by the ever-rising volume and velocity of streaming data, underscore the vital need for time series analytics. In this regard, time-series anomaly detection has been an important activity, entailing various applications in fields such as cyber security, financial markets, law enforcement, and health care. While traditional literature on anomaly detection is centered on statistical measures, the increasing number of machine learning algorithms in recent years call for a structured, general characterization of the research methods for time-series anomaly detection. This survey groups and summarizes anomaly detection existing solutions under a process-centric taxonomy in the time series context. In addition to giving an original categorization of anomaly detection methods, we also perform a meta-analysis of the literature and outline general trends in time-series anomaly detection research.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Dec 2024.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 9; 2026: 14.

23 papers.

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  1. 2025Time-RA: Towards Time Series Reasoning for Anomaly Diagnosis with LLM Feedbackcited by 7
  2. 2025mTSBench: Benchmarking Multivariate Time Series Anomaly Detection and Model Selection at Scalecited by 6
  3. 2025Building Machine Learning Challenges for Anomaly Detection in Sciencecited by 5
  4. 2026PaAno: Patch-Based Representation Learning for Time-Series Anomaly Detectioncited by 5
  5. 2026Adaptive Conformal Anomaly Detection with Time Series Foundation Models for Signal Monitoringcited by 3
  6. 2025Unsupervised Feature Construction for Anomaly Detection in Time Series -- An Evaluationcited by 1
  7. 2025CoCAI: Copula-based Conformal Anomaly Identification for Multivariate Time-Seriescited by 1
  8. 2025MSAD: A Deep Dive into Model Selection for Time series Anomaly Detectioncited by 1
  9. 2026Disjoint or Overlapping? Inference Windowing for Reconstruction-Based Time Series Anomaly Detectioncited by 1
  10. 2025Real-Time Decorrelation-Based Anomaly Detection for Multivariate Time Seriescited by 0
  11. 2025AnoF-Diff: One-Step Diffusion-Based Anomaly Detection for Forceful Tool Usecited by 0
  12. 2025A Comprehensive Forecasting-Based Framework for Time Series Anomaly Detection: Benchmarking on the Numenta Anomaly Benchmark (NAB)cited by 0
11 more
  1. 2026Time-Series Anomaly Classification for Launch Vehicle Propulsion Systems: Fast Statistical Detectors Enhancing LSTM Accuracy and Data Qualitycited by 0
  2. 2026GNNs for Time Series Anomaly Detection: An Open-Source Framework and a Critical Evaluationcited by 0
  3. 2026EngineAD: A Real-World Vehicle Engine Anomaly Detection Datasetcited by 0
  4. 2026ClouDens: Operational Context-Aware Anomaly Detection for Large-scale Cloud System Monitoringcited by 0
  5. 2026TimeRLM: Recursive Language Models Enable Precise Anomaly Localization in Long-Context Time-Seriescited by 0
  6. 2026PRISM: Powerful Time Series to Image (TS2I) Representations for Multivariate Anomaly Detectioncited by 0
  7. 2026Continuous Online Fault Detection for Mobile Robots via Adaptive Edge Modelscited by 0
  8. 2026In a Streaming World, Should You Stand Still? A Comprehensive Benchmark of Anomaly Detection in Streamscited by 0
  9. 2026What Streaming Anomaly Detection Finds (and Misses) in Industrial Time Seriescited by 0
  10. 2026From Benchmarks to Production: Transferring Time Series Anomaly Detection Methods for Electricity Production Monitoringcited by 0
  11. 2026Detect, Explain, Interpret: An End-to-End Benchmark for Time Series Anomaly Detection, Explainability and Interpretabilitycited by 0