Welcome to tadkit-core’s documentation!

tadkit-core

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TADkit – Timeseries Anomaly Detection kit

Website and documentation : irt-systemx.github.io/tadkit-core/

Overview

tadkit-core is a flexible and extensible Python toolkit for detecting anomalies in time-series data. It empowers data scientists and developers to quickly identify unusual patterns, monitor system behavior, and build predictive models—all with a modular design that makes integration and customization straightforward.

It builds upon scikit-learn for interfacing anomaly detection algorithms.

🔍 Key Features

• Unified Interfaces for Anomaly Detection Provides a coherent set of interfaces for different time-series anomaly detection methods. The main abstractions are:

  • Formater: prepares raw timeseries data into a machine-learning-friendly format.

  • TADLearner: enforces .fit(X), .score_samples(X), and .predict(X) coherently for unsupervised anomaly detection.

• Supports Multiple Detection Methods Includes methods from scikit-learn and Confiance.ai components (TDAAD and KCPD). All learners can be instantiated with default parameters.

• Dynamic Component Loading Only installed components are made available in the system; unavailable components are automatically skipped.

• Extensible and Modular Designed for easy integration of new anomaly detection methods and smooth scaling across different datasets and applications.

Contents:

• 📖 Guidelines
  • 🚀 Quickstart
  • 🪸⁠ Deep Dive 🪼
  • 📚 Documentation & Resources
  • Contributors and Support
• 🔄 Changelog
  • v 1.2.0 (2025-12-31, current)
  • v 1.1.1 (2025-10-23)
  • v 1.1.0 (2025-03-31)
• 🔎 Package tadkit
  • tadkit package

Indices and tables

• Index

• Module Index

• Search Page

Contributors and Support

This work has been supported by the French government under the “France 2030” program, as part of the SystemX Technological Research Institute within the Confiance.ai project.

TADkit is developed by IRT SystemX and supported by the European Trustworthy AI Association