  To observe the evolution of network traffic correlations we analyze the
eigenvalue spectra and eigenvectors statistics of delayed correlation matrices
of network traffic counts time series. Delayed correlation matrix D is composed
of the correlations between one variable in the multivariable time series and
another at a time delay \tau . Inverse participation ratio (IPR) of
eigenvectors of D deviates substantially from the IPR of eigenvectors of the
equal time correlation matrix C. We relate this finding to the localization and
discuss its importance for network congestion control. The time-lagged
correlation pattern between network time series is preserved over a long time,
up to 100\tau, where \tau=300 sec. The largest eigenvalue \lambda_{max} of D
and the corresponding IPR oscillate with two characteristic periods of 3\tau
and 6\tau . The existence of delayed correlations between network time series
fits well into the long range dependence (LRD) property of the network traffic.
  The ability to monitor and control the long memory processes is crucial since
they impact the network performance. Injecting the random traffic counts
between non-randomly correlated time series, we were able to break the picture
of periodicity of \lambda_{max}. In addition, we investigated influence of the
periodic injections on both largest eigenvalue and the IPR, and addressed
relevance of these indicators for the LRD and self-similarity of the network
traffic.
