Multivariate analysis of dynamical processes
Point processes and time series
FDM, Freiburg Center for Data Analysis and Modeling, University of Freiburg, Eckerstr. 1, 79104 Freiburg, Germany
2 Bernstein Center for Computational Neuroscience, Hansastr. 9A, 79104 Freiburg, Germany
3 Neurology Department, University Hospital of Freiburg, Breisacherstr. 64, 79110 Freiburg, Germany
Corresponding author: firstname.lastname@example.org
The analysis of multi-dimensional biomedical systems requires analysis techniques, which are able to deal with multivariate data consisting of both time series as well as point processes. Univariate and bivariate analysis techniques in the frequency domain for time series and point processes are established and investigated, although the number of investigations is strongly biased towards time series. Actual multivariate techniques for time series or hybrids of time series and point processes are scarcely addressed. Here, we present spectral analysis techniques which are able to analyse point processes as well as time series. Thereby, univariate, bivariate as well as multivariate techniques are discussed. Applications to simulated as well as real-world data reveal the abilities of the proposed techniques.
© EDP Sciences, Springer-Verlag, 2008