We present a robust graphical procedure for routine detection of isolated and patchy outliers in univariate time series. This procedure is suitable for retrospective as well as for online identification of outliers. It is based on a phase space reconstruction of the time series which allows to regard the time series as a multivariate sample with identically distributed but non independent observations. Thus, multivariate outlier identifiers can be transferred into the context of time series which is done here. Some applications to online monitoring data from intensive care are given.
Multivariate sample online monitoring outlier identification phase space reconstruction process control time series