  Recently, the visibility graph has been introduced as a novel view for
analyzing time series, which maps it to a complex network. In this paper, we
introduce new algorithm of visibility, "cross-visibility", which reveals the
conjugation of two coupled time series. The correspondence between the two time
series is mapped to a network, "the cross-visibility graph", to demonstrate the
correlation between them. We applied the algorithm to several correlated and
uncorrelated time series, generated by the linear stationary ARFIMA process.
The results demonstrate that the cross-visibility graph associated with
correlated time series with power-law auto-correlation is scale-free. If the
time series are uncorrelated, the degree distribution of their cross-visibility
network deviates from power-law. For more clarifying the process, we applied
the algorithm to real-world data from the financial trades of two companies,
and observed significant small-scale coupling in their dynamics.
