论文标题
Pysad：Python中的流媒体检测框架
PySAD: A Streaming Anomaly Detection Framework in Python
论文作者
论文摘要
流动检测需要在严格的约束下运行的算法：有界内存，单通行处理和恒定时复杂性。我们提出了Pysad，这是一个全面的Python框架，通过统一的建筑解决了这些挑战。该框架实现了17多种流媒体算法（LODA，半空间树，Xstream），其中包括投影仪，概率校准器和后处理器，包括投影仪，包括投影仪。与现有的批处理框架不同，PYSAD可以通过有限的内存进行有效的实时处理，同时保持与Pyod和Scikit-Learn的兼容性。 PYSAD支持所有学习范式和多元流的范式，在Python提供了最全面的流媒体检测工具包。源代码可在github.com/selimfirat/pysad上公开获得。
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.
