# skchange: Fast and Flexible Algorithms for Changepoint Detection

arXiv: https://arxiv.org/abs/2608.19767 · HTML: https://mlsanity.com/paper/2608.19767/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 20 references that are in the map.
- First posted on arXiv 20 Aug 2026.

## Builds on (most cited first, 9 of 9)

- [sktime: A Unified Interface for Machine Learning with Time Series](https://mlsanity.com/paper/1909.07872/) (2019)
- [On Optimal Multiple Changepoint Algorithms for Large Data](https://mlsanity.com/paper/1409.1842/) (2014)
- [Narrowest-Over-Threshold Detection of Multiple Change-points and Change-point-like Features](https://mlsanity.com/paper/1609.00293/) (2016)
- [Seeded Binary Segmentation: A general methodology for fast and optimal change point detection](https://mlsanity.com/paper/2002.06633/) (2020)
- [A computationally efficient, high-dimensional multiple changepoint procedure with application to global terrorism incidence](https://mlsanity.com/paper/2011.03599/) (2020)
- [Subset Multivariate Collective And Point Anomaly Detection](https://mlsanity.com/paper/1909.01691/) (2019)
- [Scalable changepoint and anomaly detection in cross-correlated data with an application to condition monitoring](https://mlsanity.com/paper/2010.06937/) (2020)
- [A changepoint approach to modelling non-stationary soil moisture dynamics](https://mlsanity.com/paper/2310.17546/) (2023)
- [Efficient sparsity adaptive changepoint estimation](https://mlsanity.com/paper/2306.04702/) (2023)

## Abstract (arXiv)

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 https://github.com/NorskRegnesentral/skchange.