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Eigenvalue and Eigenvector Statistics in Time Series Analysis

Barucca, P; Kieburg, M; Ossipov, A; (2020) Eigenvalue and Eigenvector Statistics in Time Series Analysis. EPL (Europhysics Letters) , 129 (6) , Article 60003. 10.1209/0295-5075/129/60003.

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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.

Type: Article
Title: Eigenvalue and Eigenvector Statistics in Time Series Analysis
DOI: 10.1209/0295-5075/129/60003
Publisher version: https://doi.org/10.1209/0295-5075/129/60003
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10074325
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