The identification of multiple outliers in online monitoring data

Abstract

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 transfered into the context of time series which is done here. Some applications to online monitoring data from intensive care are given. Key words: Multivariate sample, online monitoring, outlier identification, phase space reconstruction, process control, time series. 2 1 Introduction Increasing technical possibilities for online recording process data produce manifold challenges for statistical methods. In many fields like intensive care medicine, industrial process control, supply chain management, or electrical energy systems more and...