Revisiting Algorithms for Generating Surrogate Time Series
Papadakis, I. E.; Brinkmann, W.; Gliozzi, M.; Räth, C.
Germany, United States, Greece
Abstract
The method of surrogates is one of the key concepts of nonlinear data analysis. Here, we demonstrate that commonly used algorithms for generating surrogates often fail to generate truly linear time series. Rather, they create surrogate realizations with Fourier phase correlations leading to nondetections of nonlinearities. We argue that reliable surrogates can only be generated, if one tests separately for static and dynamic nonlinearities.