Revisiting algorithms for generating surrogate time series
Raeth, C. · Gliozzi, M. · Papadakis, I. E. · Brinkmann, W.
Data Analysis, Statistics and Probability
High Energy Astrophysical Phenomena
Computational Engineering, Finance, and Science
Chaotic Dynamics
Original · EN
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 non-detections of nonlinearities. We argue that reliable surrogates can only be generated, if one tests separately for static and dynamic nonlinearities.
English translation
This paper has no Arabic translation yet. Be the first: it takes a few seconds, and the result is stored for every future reader.