skchange: Fast and Flexible Algorithms for Changepoint Detection
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Statistics > Computation
Title:skchange: Fast and Flexible Algorithms for Changepoint Detection
Abstract:Skchange is an open-source Python library for detecting structural changes in time series. It implements modern change detection algorithms within a unified and extensible framework. The algorithms are modular and composable, and they include changepoint search methods based on both cost minimisation and statistical tests. Key features include the detection of anomalous segments in addition to changepoints; theoretically well-founded fast and approximate search methods; theoretically well-founded algorithms for high-dimensional data, covering settings where either few or many features change simultaneously; utilities for automatic and data-driven penalty calibration, which balances false alarms against missed detections; and a large collection of built-in costs and statistical tests. The design follows established scikit-learn conventions to streamline both user and contributor experience, and Numba is used extensively to achieve high computational performance. Source code and documentation are available at this https URL.
| Comments: | 6 pages, 3 figures |
| Subjects: | Computation (stat.CO); Machine Learning (cs.LG) |
| Cite as: | arXiv:2608.19767 [stat.CO] |
| (or arXiv:2608.19767v1 [stat.CO] for this version) | |
| https://doi.org/10.48550/arXiv.2608.19767
arXiv-issued DOI via DataCite
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