Title: Recurrence network measures for hypothesis testing using surrogate data: application to black hole light curves,
Abstract: Recurrence networks and the associated statistical measures have become
important tools in the analysis of time series data. In this work, we test how
effective the recurrence network measures are in analyzing real world data
involving two main types of noise, white noise and colored noise. We use two
prominent network measures as discriminating statistic for hypothesis testing
using surrogate data for a specific null hypothesis that the data is derived
from a linear stochastic process. We show that the characteristic path length
is especially efficient as a discriminating measure with the conclusions
reasonably accurate even with limited number of data points in the time series.
We also highlight an additional advantage of the network approach in
identifying the dimensionality of the system underlying the time series through
a convergence measure derived from the probability distribution of the local
clustering coefficients. As examples of real world data, we use the light
curves from a prominent black hole system and show that a combined analysis
using three primary network measures can provide vital information regarding
the nature of temporal variability of light curves from different spectroscopic
classes.