mlsanityData to 7 Oct 2026

A Meta-level Analysis of Online Anomaly Detectors

arXiv 2209.05899 ↗First posted 13 Sep 2022cs.LG cs.AIcited by 5

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Abstract (arXiv)

Real-time detection of anomalies in streaming data is receiving increasing attention as it allows us to raise alerts, predict faults, and detect intrusions or threats across industries. Yet, little attention has been given to compare the effectiveness and efficiency of anomaly detectors for streaming data (i.e., of online algorithms). In this paper, we present a qualitative, synthetic overview of major online detectors from different algorithmic families (i.e., distance, density, tree or projection-based) and highlight their main ideas for constructing, updating and testing detection models. Then, we provide a thorough analysis of the results of a quantitative experimental evaluation of online detection algorithms along with their offline counterparts. The behavior of the detectors is correlated with the characteristics of different datasets (i.e., meta-features), thereby providing a meta-level analysis of their performance. Our study addresses several missing insights from the literature such as (a) how reliable are detectors against a random classifier and what dataset characteristics make them perform randomly; (b) to what extent online detectors approximate the performance of offline counterparts; (c) which sketch strategy and update primitives of detectors are best to detect anomalies visible only within a feature subspace of a dataset; (d) what are the tradeoffs between the effectiveness and the efficiency of detectors belonging to different algorithmic families; (e) which specific characteristics of datasets yield an online algorithm to outperform all others.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 12 of its 81 references that are in the map. First posted on arXiv 13 Sep 2022.

Builds on

12 of 81 references.

References · 12

  1. 2014OpenML: networked science in machine learningcited by 523
  2. 2020A review on outlier/anomaly detection in time series datacited by 187
  3. 2020Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progresscited by 148
  4. 2018Precision and Recall for Time Seriescited by 67
  5. 2020Exathlon: A Benchmark for Explainable Anomaly Detection over Time Seriescited by 56
  6. 2020Anomaly Detection in Univariate Time-series: A Survey on the State-of-the-Artcited by 55
  7. 2015A Survey of Predictive Modelling under Imbalanced Distributionscited by 23
  8. 2016MacroBase: Prioritizing Attention in Fast Datacited by 15
  9. 2020Automating Outlier Detection via Meta-Learningcited by 9
  10. 2017On the Runtime-Efficacy Trade-off of Anomaly Detection Techniques for Real-Time Streaming Datacited by 5
  11. 2016One Class Splitting Criteria for Random Forestscited by 4
  12. 2016Local Subspace-Based Outlier Detection using Global Neighbourhoodscited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 1; 2026: 2.

5 papers.

  1. 2024A Closer Look at AUROC and AUPRC under Class Imbalancecited by 56
  2. 2023From Explanation to Action: An End-to-End Human-in-the-loop Framework for Anomaly Reasoning and Managementcited by 2
  3. 2025A Comparative Analysis of Influence Signals for Data Debuggingcited by 0
  4. 2026Catching Every Ripple: Enhanced Anomaly Awareness via Dynamic Concept Adaptationcited by 0
  5. 2026In a Streaming World, Should You Stand Still? A Comprehensive Benchmark of Anomaly Detection in Streamscited by 0