Welcome to TSADmetrics - Time Series Anomaly Detection Metrics

TSADmetrics is a Python library for evaluating anomaly detection algorithms in time series data. It provides a comprehensive set of binary and non-binary metrics designed specifically for the challenges of anomaly detection in temporal contexts.

Documentation Contents

Contents:

• Installation
  • Requirements
• Uninstallation
  • Notes
• Using TSADmetrics
  • Example: Direct Data
  • Example: Direct Single Data
  • Example: File Reference
  • Example: Metric Config File
  • Example: Global Config File
  • Direct Metric Usage

• Specific Metric Usage

• API Reference
  • Base
  • Evaluation
  • Metrics

Acknowledgements

This library was supported in part by the PID2023-148396NB-I00 project of Spanish Ministry of Science and Innovation and the European Regional Development Fund, by the ProyExcel-0069 project of the Andalusian University, Research and Innovation Department.

References

This library is based on the concepts and implementations from: Sørbø, S., & Ruocco, M. (2023). Navigating the metric maze: a taxonomy of evaluation metrics for anomaly detection in time series. https://doi.org/10.1007/s10618-023-00988-8