Automated detection of time-dependent cross-correlation clusters in nonstationary time series

¹  Facultad de Ciencias, Universidad Autónoma del Estado de Morelos - 62209 Cuernavaca, México
²  Manchester Interdisciplinary Biocentre, The University of Manchester - 131 Princess Street, Manchester, UK
³  EML Research - Schloss-Wolfsbrunnenweg 33, 69118 Heidelberg, Germany

Corresponding author: crummel@web.de

Received: 7 August 2007
Accepted: 19 October 2007

Abstract

A novel method for the detection of cross-correlation clusters in multivariate time series is suggested. It is based on linear combinations of the eigenvectors corresponding to the largest eigenvalues of the equal-time cross-correlation matrix. The linear combinations are found in a systematic way by maximizing an appropriate distance measure. The performance of the algorithm is evaluated with a flexible time-seriesbased test framework for cluster algorithms. Attribution errors are investigated quantitatively in model data and a comparison with three alternative approaches is made. As the algorithm is suitable for unsupervised online application we demonstrate its time-resolved use in the example of cluster detection in time series from human electroencephalogram.