arXiv:2606. 20216v1 Announce Type: cross Abstract: Machine learning algorithms deployed for evolving streaming environments must handle the non-stationary data distributions, commonly referred to as concept drift.
By Md Moman Ul Haque Khan, Samira Sadaoui
arXiv:2606. 07789v1 Announce Type: new Abstract: Data stream mining is fundamentally challenged by concept drift, where distributional changes can degrade model performance.
By Vitor Cerqueira, Heitor Murilo Gomes, Marco Heyden, Bernhard Pfahringer, Albert Bifet
MORA is a drift‑robust time‑series anomaly detection framework that addresses the ambiguity between distribution drift and true anomalies by defining a temporal change disambiguation problem. It reconstructs a local target from paired short‑ and long‑term views, using the reconstruction gap as a measure of contextual support to adjust the primary anomaly score. The method requires no drift annotations or online adaptation and demonstrates strong robustness to non‑stationarity while maintaining sensitivity to genuine anomalies on four TSAD benchmarks.
By Xudong Mou, Tiejun Wang, Rui Wang, Hui Wang, Pin Liu, Tianyu Wo, Xudong Liu, Renyu Yang
arXiv:2608. 19767v1 Announce Type: cross Abstract: Skchange is an open-source Python library for detecting structural changes in time series.
By Martin Tveten, Johannes Voll Kolst{\o}, Per August Jarval Moen
arXiv:2607. 17336v1 Announce Type: new Abstract: Drift detection is a core component of production machine learning monitoring systems, where detectors are used to compare incoming data with a reference distribution and trigger alerts when changes occur.
By Raj Shekhar Singh
skchange is an open‑source Python library that provides fast and flexible algorithms for detecting structural changes in time series. It offers modular, composable methods based on cost minimisation and statistical tests, and includes features such as anomalous segment detection, high‑dimensional data support, automatic penalty calibration, and a wide range of built‑in costs and tests. The library follows scikit‑learn conventions and uses Numba for high computational performance, with source code and documentation available on GitHub.