9 Anomaly Detection Tools and Libraries for Time Series Data

Open Source Anomaly Detection Tools

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  adtk

  

  A Python toolkit for rule-based/unsupervised anomaly detection in time series.

  License: Mozilla Public License 2.0

  GitHub
  Website: https://adtk.readthedocs.io
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  Alibi Detect

  

  alibi-detect is a Python package focused on outlier, adversarial and concept drift detection.

  License: Apache License 2.0

  GitHub
  Website: https://docs.seldon.io/projects/alibi-detect/en/stable/
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  dBoost

  

  Outlier detection in heterogeneous datasets using automatic tuple expansion.

  License: GNU General Public License v3.0

  GitHub
  
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  Deequ

  

  A library built on top of Apache Spark for defining “unit tests for data”, which measure data quality in large datasets.

  License: Apache License 2.0

  GitHub
  
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  Deep Anomaly Detection with Outlier Exposure

  

  Outlier Exposure (OE) is a method for improving anomaly detection performance in deep learning models.

  License: Apache License 2.0

  GitHub
  
• 

  PyOD

  

  A Python Toolbox for Scalable Outlier Detection (Anomaly Detection).

  License: BSD 2-Clause "Simplified" License

  GitHub
  Website: http://pyod.readthedocs.io
• 

  SUOD (Scalable Unsupervised Outlier Detection)

  

  An Acceleration System for Large-scale Anomaly/Outlier Detection.

  License: BSD 2-Clause "Simplified" License

  GitHub
  Website: https://www.andrew.cmu.edu/user/yuezhao2/papers/20-preprint-suod.pdf
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  Tensorflow Data Validation (TFDV)

  

  Library for exploring and validating machine learning data.

  License: Apache License 2.0

  GitHub
  
• 

  TODS

  

  TODS is a full-stack automated machine learning system for outlier detection on multivariate time-series data.

  License: Apache License 2.0

  GitHub
  Website: http://tods-doc.github.io

Last Updated: Dec 26, 2023