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: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: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...
By Sven Jacob, Bardh Prenkaj, Weijia Shao, Gjergji Kasneci
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...
By Xiaokai Luo, Chenghao Xu, Haotian Xu, Carlos Misael Madrid Padilla, Daren Wang
arXiv:2608. 13922v1 Announce Type: new Abstract: Detecting distributional changes in high dimension is difficult when neither the pre-change nor post-change density is parametrically specified.
By Guoqing Zhang, Zhaixin Chen
arXiv:2606. 07151v1 Announce Type: new Abstract: Traditional change point detection in dynamic networks assumes abrupt transitions between stationary states, overlooking scenarios of continuous evolution which arise in most real-world applications, such as social networks or physical systems.
By William Cappelletti, \'Etienne Voutaz, Pascal Frossard
The paper introduces a sequential change‑point detection method for time‑ordered data where neither the pre‑ nor post‑change distributions have closed forms. It trains a conditional diffusion model on pre‑change data, uses its probability flow ODE to map observations to a Gaussian latent space, and then applies the Maximum Mean Discrepancy as a test statistic. The authors derive closed‑form components under the Gaussian null, establish the statistic’s asymptotic distribution as a degenerate U‑statistic, and implement an online Shiryaev–Roberts procedure with exact threshold calibration to detect arbitrary distributional shifts without parametric assumptions.
By Artem Kraevskiy, Artem Prokhorov
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.
By Richard Song
arXiv:2606. 01256v1 Announce Type: cross Abstract: This paper introduces a distribution-free framework for constructing post-detection confidence sets for changepoints after stopping a sequential change detection procedure.
By Aytijhya Saha, Aaditya Ramdas
arXiv:2605.31187v2 Announce Type: replace-cross
Abstract: Detecting covariate shift is critical for building reliable vision systems. While most prior work focuses on improving robustness to shift, e...
By Firas Gabetni, Alexandre Rocchi, Nacim Belkhir, Ziyi Liu, Gianni Franchi
arXiv:2605. 03723v2 Announce Type: replace-cross Abstract: The rise of large language models (LLMs) has created an urgent need to distinguish between human-written and LLM-generated text to ensure authenticity and societal trust.
By Mengchu Li, Jin Zhu, Jinglai Li, Chengchun Shi
arXiv:2606. 31230v1 Announce Type: new Abstract: We study the task of learning the structure of a $d$-sparse Gaussian graphical model on $n$ variables from a single trajectory of Glauber dynamics.
By Eric Shen, Tony Wu, Mahbod Majid, Ankur Moitra