论文标题
TEGDET：可扩展的python库，用于使用时间变化图的异常检测
tegdet: An extensible Python Library for Anomaly Detection using Time-Evolving Graphs
论文作者
论文摘要
本文提出了一个新的Python库，用于无监督学习方法中的异常检测。库的输入是一个单变量时间序列，代表了给定现象的观察。然后，它可以识别异常时期，即时间间隔，其中观测值高于基线分布的给定百分比，该分布由差异度量定义。使用随时间变化的图进行异常检测，该库利用数据之间的相互依存关系的有价值信息。目前，图书馆实现了28个不同的差异指标，并且已设计为易于使用新的差异指标。通过API，库公开了一个完整的功能以执行异常检测。总而言之，据我们所知，该库是唯一可以使用动态图的公开可用的库，可以通过其他最新的异常检测技术扩展。我们的实验显示了有关算法的执行时间和实施技术的准确性的有希望的结果。此外，本文提供了设置检测器参数以提高其性能和预测准确性的准则。
This paper presents a new Python library for anomaly detection in unsupervised learning approaches. The input for the library is a univariate time series representing observations of a given phenomenon. Then, it can identify anomalous epochs, i.e., time intervals where the observations are above a given percentile of a baseline distribution, defined by a dissimilarity metric. Using time-evolving graphs for the anomaly detection, the library leverages valuable information given by the inter-dependencies among data. Currently, the library implements 28 different dissimilarity metrics, and it has been designed to be easily extended with new ones. Through an API, the library exposes a complete functionality to carry out the anomaly detection. Summarizing, to the best of our knowledge, this library is the only one publicly available, that based on dynamic graphs, can be extended with other state-of-the-art anomaly detection techniques. Our experimentation shows promising results regarding the execution times of the algorithms and the accuracy of the implemented techniques. Additionally, the paper provides guidelines for setting the parameters of the detectors to improve their performance and prediction accuracy.
