title: A generalization of random matrix theory and its application to statistical physics.
abstract: To study the statistical structure of crosscorrelations in empirical data, we generalize random matrix theory and propose a new method of cross-correlation analysis, known as autoregressive random matrix theory (ARRMT). ARRMT takes into account the influence of auto-correlations in the study of cross-correlations in multiple time series. We first analytically and numerically determine how auto-correlations affect the eigenvalue distribution of the correlation matrix. Then we introduce ARRMT with a detailed procedure of how to implement the method. Finally, we illustrate the method using two examples taken from inflation rates for air pressure data for 95 US cities.
authors.affiliation: Center for Polymer Studies and Department of Physics, Boston University, Boston, Massachusetts 02215, USA.
authors.forename: Duan
authors.initials: D
authors.lastname: Wang
authors.affiliation: College of Communication and Transport, Shanghai Maritime University, Shanghai 201306, China.
authors.forename: Xin
authors.initials: X
authors.lastname: Zhang
authors.affiliation: Physics Department, Faculty of Science, University of Zagreb, Bijenička c. 32, 10000 Zagreb, Croatia.
authors.forename: Davor
authors.initials: D
authors.lastname: Horvatic
authors.affiliation: Center for Polymer Studies and Department of Physics, Boston University, Boston, Massachusetts 02215, USA.
authors.forename: Boris
authors.initials: B
authors.lastname: Podobnik
authors.affiliation: Center for Polymer Studies and Department of Physics, Boston University, Boston, Massachusetts 02215, USA.
authors.forename: H
authors.initials: H
authors.lastname: Eugene Stanley