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Unsupervised Anomaly Detection for IoT-Based Multivariate Time Series: Existing Solutions, Performance Analysis and

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Detecting anomalies in multivariate time series data is essential for system monitoring. This review covers unsupervised methods for multivariate time series anomaly detection (MTSAD), evaluating 13 algorithms on real-world data.

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Area of Science:

  • Computer Science
  • Data Science
  • Engineering

Background:

  • Digitalization drives sensor deployment, generating vast unlabeled multivariate time series data.
  • Multivariate Time Series Anomaly Detection (MTSAD) is crucial for identifying system anomalies but faces challenges in analyzing temporal and spatial dependencies.
  • Unsupervised MTSAD is highly desirable due to the impracticality of labeling massive datasets.

Purpose of the Study:

  • To provide a comprehensive review of the state-of-the-art in unsupervised Multivariate Time Series Anomaly Detection (MTSAD).
  • To offer a theoretical background on MTSAD techniques.
  • To present a numerical evaluation of prominent unsupervised MTSAD algorithms.

Main Methods:

  • Review of advanced machine learning, signal processing, and deep learning techniques for unsupervised MTSAD.
  • Theoretical exposition of MTSAD principles, emphasizing temporal and spatial dependency analysis.
  • Comparative numerical evaluation of 13 selected unsupervised MTSAD algorithms using two public datasets.

Main Results:

  • Identification of key challenges and advancements in unsupervised MTSAD.
  • Performance comparison of 13 algorithms, highlighting their strengths and weaknesses.
  • Empirical evidence on the effectiveness of various unsupervised approaches for MTSAD.

Conclusions:

  • Unsupervised MTSAD methods are critical for handling large-scale, unlabeled sensor data in industrial and other applications.
  • The reviewed algorithms offer diverse capabilities for detecting anomalies in multivariate time series.
  • Further research is needed to address the complexities of simultaneous temporal and spatial dependency analysis in MTSAD.