Multivariate Time Series Analysis

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Abstract

Nowadays, modern measurement devices are capable to deliver signals with increasing data rates and higher spatial resolutions. When analyzing these data, particular interest is focused on disentangling the network structure underlying the recorded signals. Neither univariate nor bivariate analysis techniques are expected to describe the interactions between the processes sufficiently well. Moreover, the direction of the direct interactions is particularly important to understand the underlying network structure sufficiently well. Here, we present multivariate approaches to time series analysis being able to distinguish direct and indirect, in some cases the directions of interactions in linear as well as nonlinear systems.