Statistics and Computing

, Volume 25, Issue 5, pp 913–928 | Cite as

Robust online-surveillance of trend-coherence in multivariate data streams: the similar trend monitoring (STM) procedure

  • Matthias BorowskiEmail author
  • Dennis Busse
  • Roland Fried


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.


Multivariate data streams Coherence Trend estimation Online Robustness 


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Institute of Biostatistics and Clinical ResearchUniversity of MünsterMünsterGermany
  2. 2.Chrestos Concept GmbH & Co. KGRatingenGermany
  3. 3.Faculty of StatisticsTU Dortmund UniversityDortmundGermany

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