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首页> 外文期刊>Statistics and computing >Robust online-surveillance of trend-coherence in multivariate data streams: the similar trend monitoring (STM) procedure
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Robust online-surveillance of trend-coherence in multivariate data streams: the similar trend monitoring (STM) procedure

机译：多元数据流中趋势一致性的强大在线监视：相似趋势监视（STM）过程

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摘要

When several data streams are observed simultaneously, it is often of great interest to monitor the coherences between all pairs of streams. We propose a new technique called Similar Trend Monitoring (STM) for this task: The current slopes of all univariate streams are estimated and compared pairwise at each time point. The STM statistic is the standardized slope difference, so that decisions about coherence can be made by means of the six-sigma-rule, for instance. The STM meets the high demands that come along with the online monitoring of multivariate data streams: it is fast to compute, robust against outliers, applicable when observations are missing, and does not require stationarity of the processes. We investigate the distribution and the performance of the STM and demonstrate its capabilities considering blood pressure time series from intensive care patient monitoring as an example.
机译：当同时观察到多个数据流时，监视所有流对之间的一致性通常是非常重要的。我们为此任务提出了一种称为类似趋势监测（STM）的新技术：估计并在每个时间点成对比较所有单变量流的当前斜率。 STM统计数据是标准化的斜率差异，因此可以通过例如6-sigma规则来做出有关连贯性的决定。 STM满足了对多变量数据流进行在线监视的高要求：它计算速度快，对异常值具有鲁棒性，可在缺少观测值时使用，并且不需要过程的平稳性。我们研究了STM的分布和性能，并以重症监护患者监测为例，考虑了血压时间序列，论证了其功能。

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