论文标题
实时异常检测和分类
Real Time Anomaly Detection And Categorisation
论文作者
论文摘要
在数据序列中快速准确检测异常结构的能力是重要性越来越重要的推论挑战。这项工作将最近提出的事后（离线）异常检测方法扩展到顺序设置。最终的过程能够在基线和两种异常结构之间进行实时分析和分类：点和集体异常。该过程的各种理论特性得出。这些以及一项广泛的仿真研究强调，平均运行长度到虚假警报，而所提出的在线算法的平均检测延迟非常接近离线版本的。提供了模拟和实际数据的实验，以证明该方法的好处。
The ability to quickly and accurately detect anomalous structure within data sequences is an inference challenge of growing importance. This work extends recently proposed post-hoc (offline) anomaly detection methodology to the sequential setting. The resultant procedure is capable of real-time analysis and categorisation between baseline and two forms of anomalous structure: point and collective anomalies. Various theoretical properties of the procedure are derived. These, together with an extensive simulation study, highlight that the average run length to false alarm and the average detection delay of the proposed online algorithm are very close to that of the offline version. Experiments on simulated and real data are provided to demonstrate the benefits of the proposed method.
