Abstract
In intensive care, clinical information systems permanently record more than one hundred time dependent variables. Besides the aim of recognising patterns like outliers, level changes and trends in such high-dimensional time series, it is important to reduce their dimension and to understand the possibly time-varying dependencies between the variables. We discuss statistical procedures which are able to detect patterns of dependencies within multivariate time series.
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Gather, U., Fried, R., Imhoff, M., Becker, C. (2002). Patterns of Dependencies in Dynamic Multivariate Data. In: Hand, D.J., Adams, N.M., Bolton, R.J. (eds) Pattern Detection and Discovery. Lecture Notes in Computer Science(), vol 2447. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45728-3_17
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DOI: https://doi.org/10.1007/3-540-45728-3_17
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