Eigenvalue and Eigenvector Statistics in Time Series Analysis
Statistical Mechanics
2020-07-28 v1 Disordered Systems and Neural Networks
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.
Cite
@article{arxiv.1904.05079,
title = {Eigenvalue and Eigenvector Statistics in Time Series Analysis},
author = {Paolo Barucca and Mario Kieburg and Alexander Ossipov},
journal= {arXiv preprint arXiv:1904.05079},
year = {2020}
}
Comments
8 pages, 3 figures