arXiv:2608. 19767v1 Announce Type: cross Abstract: Skchange is an open-source Python library for detecting structural changes in time series.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
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.
arXiv:2512. 07541v3 Announce Type: replace-cross Abstract: Inspired by graph-based methodologies, we introduce a novel graph-spanning algorithm designed to identify changes in both offline and online data across low to high dimensions.
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arXiv:2609. 15479v1 Announce Type: cross Abstract: This paper studies the detection of multiple simultaneous (systematic) change points for high-dimensional nonstantionary economic and financial time series data.
arXiv:2609.36594v1 Announce Type: cross Abstract: We study multiple change-point detection in multivariate time series whose distributions change in a piecewise constant manner. Distributional change...
arXiv:2609.24278v1 Announce Type: new Abstract: Change point detection (CPD) identifies abrupt and significant changes in sequential data, with applications in human activity recognition, financial m...