论文标题
可扩展的更改点和交叉数据中的异常检测，并应用于调理监视
Scalable changepoint and anomaly detection in cross-correlated data with an application to condition monitoring
论文作者
论文摘要
由海底工程引起的条件监测应用的激励，我们得出了一种可扩展的新方法，用于检测相关的多元时间序列子集中的异常平均结构。鉴于需要有效地分析此类系列，我们探索了最大似然解决方案对所得建模框架的计算有效近似，并开发出一种新的动态编程算法，以在任何给定时间点的时间序列的精确时间序列的精确矩阵时，以求解所得的二进制二进制次数矩阵。通过一项全面的仿真研究，我们表明，即使精确矩阵估计的稀疏结构被误解了，与异常和变化检测设置的竞争方法相比，所产生的方法的性能相比。我们还展示了其在激励应用程序中正确检测泵的时间周期故障的能力。
Motivated by a condition monitoring application arising from subsea engineering we derive a novel, scalable approach to detecting anomalous mean structure in a subset of correlated multivariate time series. Given the need to analyse such series efficiently we explore a computationally efficient approximation of the maximum likelihood solution to the resulting modelling framework, and develop a new dynamic programming algorithm for solving the resulting Binary Quadratic Programme when the precision matrix of the time series at any given time-point is banded. Through a comprehensive simulation study, we show that the resulting methods perform favourably compared to competing methods both in the anomaly and change detection settings, even when the sparsity structure of the precision matrix estimate is misspecified. We also demonstrate its ability to correctly detect faulty time-periods of a pump within the motivating application.
