Volume 80, Number 6, December 2007
|Number of page(s)||6|
|Section||Interdisciplinary Physics and Related Areas of Science and Technology|
|Published online||13 November 2007|
Automated detection of time-dependent cross-correlation clusters in nonstationary time series
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: firstname.lastname@example.org
Accepted: 19 October 2007
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.
PACS: 87.19.La – Neuroscience / 05.45.Tp – Time series analysis / 89.75.Fb – Structures and organization in complex systems
© EPLA, 2007
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