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arXiv · 1904.05079

Eigenvalue and Eigenvector Statistics in Time Series Analysis

Paolo Barucca·Mario Kieburg·Alexander Ossipov
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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.

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BibTeXRIS

Paolo Barucca, Mario Kieburg, Alexander Ossipov. 2019-04-10. Eigenvalue and Eigenvector Statistics in Time Series Analysis. https://doi.org/10.1209/0295-5075/129/60003

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