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Expert enhanced dynamic time warping based anomaly detection

arXiv 2310.02280 ↗First posted 2 Oct 2023cs.LG cs.AIcited by 0

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Abstract (arXiv)

Dynamic time warping (DTW) is a well-known algorithm for time series elastic dissimilarity measure. Its ability to deal with non-linear time distortions makes it helpful in variety of data mining tasks. Such a task is also anomaly detection which attempts to reveal unexpected behaviour without false detection alarms. In this paper, we propose a novel anomaly detection method named Expert enhanced dynamic time warping anomaly detection (E-DTWA). It is based on DTW with additional enhancements involving human-in-the-loop concept. The main benefits of our approach comprise efficient detection, flexible retraining based on strong consideration of the expert's detection feedback while retaining low computational and space complexity.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 80 references that are in the map. First posted on arXiv 2 Oct 2023.

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References · 11

  1. 2018GANomaly: Semi-Supervised Anomaly Detection via Adversarial Trainingcited by 409
  2. 2021A Survey of Human-in-the-loop for Machine Learningcited by 215
  3. 2020A review on outlier/anomaly detection in time series datacited by 189
  4. 2019A Survey on Active Learning and Human-in-the-Loop Deep Learning for Medical Image Analysiscited by 151
  5. 2015Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly Benchmarkcited by 88
  6. 2018Extended Isolation Forestcited by 81
  7. 2018ECG Heartbeat Classification: A Deep Transferable Representationcited by 76
  8. 2021RLAD: Time Series Anomaly Detection through Reinforcement Learning and Active Learningcited by 13
  9. 2020A Survey of Machine Learning Methods for Detecting False Data Injection Attacks in Power Systemscited by 8
  10. 2017Incorporating Feedback into Tree-based Anomaly Detectioncited by 7
  11. 2022Little Help Makes a Big Difference: Leveraging Active Learning to Improve Unsupervised Time Series Anomaly Detectioncited by 4

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