发表机构
Norwegian Computing Center; University of Oslo（挪威计算中心; 奥斯陆大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
skchange是遵循scikit-learn规范、基于Numba加速的开源Python库，实现模块化变点检测算法，支持高维数据等场景，提供自动惩罚校准等功能，用于时间序列结构变化检测。
AI 中文摘要
skchange是一个用于检测时间序列结构变化的开源Python库，它在统一且可扩展的框架内实现了现代变点检测算法。这些算法是模块化和可组合的，包括基于成本最小化和统计检验的变点搜索方法。其关键特性除了检测变点外，还能检测异常段；具备理论支撑的快速近似搜索方法；适用于高维数据的理论支撑算法，涵盖少量或大量特征同时变化的场景；提供自动且数据驱动的惩罚校准工具，可平衡误报与漏检；还有大量内置成本函数和统计检验。其设计遵循成熟的scikit-learn规范，以简化用户和贡献者的使用体验，且广泛使用Numba来实现高计算性能。源代码和文档可在指定网址获取。
英文摘要
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.
Comments6 pages, 3 figures
