Volume 129, Number 6, March 2020
|Number of page(s)||6|
|Published online||21 April 2020|
Eigenvalue and eigenvector statistics in time series analysis
1 Department of Computer Science, University College London - London WC1E 6EA, UK
2 Faculty of Physics, Bielefeld University - P.O. Box 100131, D-33501 Bielefeld, Germany
3 School of Mathematical Sciences, University of Nottingham - Nottingham NG7 2RD, UK
Received: 14 October 2019
Accepted: 7 April 2020
The study of correlated time series is ubiquitous in statistical analysis, and the matrix decomposition of the cross-correlations between time series is a universal tool to extract the principal patterns of behavior in a wide range of complex systems. Despite this fact, no general result is known for the statistics of eigenvectors of the cross-correlations of correlated time series. Here we use supersymmetric theory to provide novel analytical results that will serve as a benchmark for the study of correlated signals for a vast community of researchers.
PACS: 05.45.Tp – Time series analysis / 02.10.Yn – Matrix theory / 02.50.-r – Probability theory, stochastic processes, and statistics
© EPLA, 2020
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.