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Eigenvalue and eigenvector statistics in time series analysis

Barucca, Paolo; Kieburg, Mario; Ossipov, Alexander

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Authors

Paolo Barucca

Mario Kieburg

Alexander Ossipov



Abstract

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.

Citation

Barucca, P., Kieburg, M., & Ossipov, A. (2020). Eigenvalue and eigenvector statistics in time series analysis. EPL, 129(6), https://doi.org/10.1209/0295-5075/129/60003

Journal Article Type Article
Acceptance Date Apr 7, 2020
Online Publication Date Apr 21, 2020
Publication Date 2020-03
Deposit Date May 19, 2020
Publicly Available Date Mar 28, 2024
Journal EPL (Europhysics Letters)
Print ISSN 1286-4854
Publisher EPL Association
Peer Reviewed Peer Reviewed
Volume 129
Issue 6
Article Number 60003
DOI https://doi.org/10.1209/0295-5075/129/60003
Keywords General Physics and Astronomy
Public URL https://nottingham-repository.worktribe.com/output/4469750
Publisher URL https://iopscience.iop.org/article/10.1209/0295-5075/129/60003

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