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.

翻译：我们利用海底工程产生的状况监测应用,在相关多变时间序列的子集中,以新颖、可扩缩的方法探测异常平均结构。鉴于需要高效分析此类序列,我们探索如何以计算高效的近似最大可能性解决方案接近生成的模型框架,并开发新的动态编程算法,在任何特定时间点的时间序列精确矩阵被拉动时,解决由此产生的二元二次二次曲线方案。我们通过全面模拟研究,显示所产生的方法与异常和变化探测环境中的相竞方法相比效果良好,即使精确矩阵估计的宽度结构被错误描述。我们还表明它有能力在激励应用程序中正确发现泵的错误时间段。