DANTIS documentation

DANTIS (Detection of ANomalies in TIme Series) is a Python library that unifies over 50 anomaly detection algorithms under a single, consistent API. It supports statistical, machine learning, and deep learning approaches, and includes both programmatic usage and a graphical interface for experimentation.

This documentation provides a complete guide to using DANTIS, from installation to advanced usage and extension.

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📚 Reference

• DANTIS API Reference
  • dantis.utils
• Guide for Adding New Algorithms
  • 1. General Structure
  • 2. Inheriting from AlgorithmBase
  • 3. Required Methods
  • 4. Hyperparameter Management
  • 5. Model Creation
  • 6. Saving and Loading the Model
  • 7. Minimal Example
  • 8. Recommendations

🧠 Citation: If you use DANTIS in academic work, please cite it as described in the README (https://github.com/kdis-lab/dantis)

🔗 Repository: https://github.com/kdis-lab/dantis 📖 Web Documentation: https://dantis.readthedocs.io