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.
By Katerina Papagiannouli, Yang-wen Sun, Vladimir Spokoiny
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: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: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: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:2607. 24673v1 Announce Type: new Abstract: We describe Causal-TS, an open-source Python library for causal discovery in high-dimensional and nonstationary multivariate time series.
By Mohammad Fesanghary
We describe Causal-TS, an open-source Python library for causal discovery in high-dimensional and nonstationary multivariate time series. Causal-TS provides four specialized algorithms-CDNOTS, CDNOTS+, CEDAR, and GRACE-along with wrappers for GES, Granger, LASSO-VAR, and LGES, all sharing a unified conditional independence (CI) test layer with GPU acceleration via PyTorch.
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
The paper introduces MaSoN, an end-to-end unsupervised remote sensing change detection framework that synthesises diverse changes directly in latent feature space during training. By generating changes based on feature statistics of the target data, MaSoN produces data‑driven variations that align with the target domain and can be applied to new modalities such as SAR and multispectral imagery. The method achieves a 14.1 percentage point improvement in average F1 score across five benchmarks, demonstrating strong generalisation across diverse change types.
By Bla\v{z} Rolih, Matic Fu\v{c}ka, Filip Wolf, Luka \v{C}ehovin Zajc
arXiv:2608. 16659v1 Announce Type: cross Abstract: Ensembles of decision trees are well-established methods for data stream classification.
By Daniel Nowak Assis, Jean Paul Barddal, Fabr\'icio Enembreck
EvoTS-Agent is a self‑evolving large language model agent designed for autonomous change‑point detection in financial time series. It begins with curated exploratory data analysis to set up candidate models, then iteratively refines its detection pipeline using three operators—Revision, Alternative Strategy, and Recombination—guided by validation feedback. Across four benchmark datasets, EvoTS-Agent consistently outperforms existing LLM‑based agents and achieves a 100% execution success rate on all tested backbone LLMs.
By Lei Jiang, Ye Wei, Xinyu Xi, Jordan Langham-Lopez, Yifan Bao, Raad Khraishi, Yihao Ang, Anthony K. H. Tung, Lukasz Szpruch, Hao Ni