快速检测时间序列突变点，支持多种算法和高维数据。
skchange: Fast and Flexible Algorithms for Changepoint Detection
- 基于代价最小化与统计检验的模块化检测方法
- 可识别异常段，支持高维数据中多特征同步突变
- 自动调参与高效计算，适合工业级时间序列分析
Skchange 是一个开源 Python 库，用于检测时间序列中的结构突变。它在统一且可扩展的框架内实现了现代突变检测算法，支持基于代价最小化和统计检验的搜索方法。该库能检测异常段而非仅突变点；提供理论严谨的快速近似搜索算法，适用于高维数据，涵盖少数或大量特征同时变化的情形；内置自动与数据驱动的惩罚参数校准工具，平衡误报与漏检；包含丰富的预置代价函数和统计检验方法。设计遵循 scikit-learn 常规，便于用户与贡献者使用，广泛采用 Numba 加速计算性能。源码与文档见 https://github.com/NorskRegnesentral/skchange。
原文摘要 · Abstract (English)
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.
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