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.

快速准确地检测数据序列中异常结构的能力是一项日益重要的推理挑战。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。由此产生的程序能够在基线和两种形式的异常结构之间进行实时分析和分类：点异常和集体异常。推导出该过程的各种理论性质。这些，再加上广泛的模拟研究，突出表明所提出的在线算法的平均误报运行时间和平均检测延迟非常接近离线版本。提供了模拟和真实数据的实验来证明所提出方法的好处。