Abstract
In our work we studied the nonlinear interdependence metric quantifying the mutual dynamics of two stochastic data series. This metric is based on the calculation of the Euclidean distances between points belonging to the trajectories of these series in the state-space. Using surrogate data as an example, the sensitivity of the metric to the autocorrelation properties of the studied data series, as well as to the amplitude and phase randomization, are investigated. We also considered the application of this metric to the analysis of backscatter signals in sea surface monitoring. We suggest that the nonlinear interdependence metric may be useful as a complementary indicator for the sea wave structure quantification and modeling.
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