PySAD：Python 中的流式异常检测框架
PySAD: A Streaming Anomaly Detection Framework in Python
AI总结：
PySAD 是一个 Python 流式异常检测框架，通过统一架构实现有界内存、单遍处理和常数时间复杂度的实时检测，集成 17+ 算法并兼容 PyOD 和 scikit-learn。
摘要：
流式异常检测需要算法在严格约束下运行：有界内存、单遍处理和常数时间复杂度。我们提出 PySAD，一个全面的 Python 框架，通过统一架构解决这些挑战。该框架实现了 17 种以上的流式算法（LODA、半空间树、xStream），并包含专门组件，如投影器、概率校准器和后处理器。与现有的以批处理为中心的框架不同，PySAD 在保持与 PyOD 和 scikit-learn 兼容性的同时，实现了有界内存的高效实时处理。支持单变量和多变量流的所有学习范式，PySAD 提供了 Python 中最全面的流式异常检测工具包。源代码公开于 github.com/selimfirat/pysad。
英文摘要：
Streaming anomaly detection requires algorithms that operate under strict constraints: bounded memory, single-pass processing, and constant-time complexity. We present PySAD, a comprehensive Python framework addressing these challenges through a unified architecture. The framework implements 17+ streaming algorithms (LODA, Half-Space Trees, xStream) with specialized components including projectors, probability calibrators, and postprocessors. Unlike existing batch-focused frameworks, PySAD enables efficient real-time processing with bounded memory while maintaining compatibility with PyOD and scikit-learn. Supporting all learning paradigms for univariate and multivariate streams, PySAD provides the most comprehensive streaming anomaly detection toolkit in Python. The source code is publicly available at github.com/selimfirat/pysad.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
