Eigenvalue and eigenvector statistics in time series analysis

Barucca, Paolo; Kieburg, Mario; Ossipov, Alexander

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), Article 60003. 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: Apr 1, 2021
• 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