The Tornado :tornado: framework, designed and implemented for adaptive online learning and data stream mining in Python.

Algorithms for detecting changes from a data stream.

An approach to apply concept drifts and ADWIN on the streams event time to reason about the progress of watermarks

Code for testing Concept drift techniques on a real word dataset on a hexapod robot

Салон штор

This project implements an AutoML-driven adaptive drift detection framework for COVID-19 forecasting, leveraging ADWIN & DDM to dynamically adjust models in response to concept drift. It enhances predictive accuracy by intelligently retraining models, ensuring robust time-series forecasting under evolving data conditions.

Streaming, drift-aware log anomaly detection with conformal calibration; reproducible, CI-tested Python and strict provenance.