论文标题
基于鲁棒投影基于单变时间序列的异常提取（RPE）
Robust Projection based Anomaly Extraction (RPE) in Univariate Time-Series
论文作者
论文摘要
本文提出了一种新颖的，封闭形式和数据/计算有效的时间序列数据的在线异常检测算法。所提出的方法称为RPE，是一种基于窗口的方法，与现有的基于窗口的方法形成鲜明对比，它在其窗口中存在异常是可靠的，并且可以在时间戳记级别区分异常。 RPE利用了时间序列的轨迹矩阵的线性结构，并采用了强大的投影步骤，该步骤使该算法能够处理其窗口中多个任意大型异常的存在。提供了可靠的投影步骤的封闭形式/非题算法，并证明它可以识别损坏的时戳。 RPE是无法提供大型培训数据的应用程序的绝佳候选者，这是时间序列领域的常见情况。一组广泛的数值实验表明，RPE可以以明显的边距胜过现有方法。
This paper presents a novel, closed-form, and data/computation efficient online anomaly detection algorithm for time-series data. The proposed method, dubbed RPE, is a window-based method and in sharp contrast to the existing window-based methods, it is robust to the presence of anomalies in its window and it can distinguish the anomalies in time-stamp level. RPE leverages the linear structure of the trajectory matrix of the time-series and employs a robust projection step which makes the algorithm able to handle the presence of multiple arbitrarily large anomalies in its window. A closed-form/non-iterative algorithm for the robust projection step is provided and it is proved that it can identify the corrupted time-stamps. RPE is a great candidate for the applications where a large training data is not available which is the common scenario in the area of time-series. An extensive set of numerical experiments show that RPE can outperform the existing approaches with a notable margin.
