2026-05-28T14:58:55Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/299252026-02-03T11:30:05Zcom_10630_2254col_10630_37953

Transforming Gaussian correlations. Applications to generating long-range power-law correlated time series with arbitrary distribution Carpena-Sánchez, Pedro Juan Bernaola-Galván, Pedro Ángel Gómez Extremera, Manuel Coronado-Jiménez, Ana Victoria Pulso cardíaco - Modelos matemáticos Procesado de señales Política de acceso abierto tomada de: https://v2.sherpa.ac.uk/id/publication/9866?template=romeo The observable outputs of many complex dynamical systems consist in time series exhibiting autocorrelation functions of great diversity of behaviors, including long-range power-law autocorre- lation functions, as a signature of interactions operating at many temporal or spatial scales. Often, numerical algorithms able to generate correlated noises reproducing the properties of real time se- ries are used to study and characterize such systems. Typically, those algorithms produce Gaussian time series. However, real, experimentally observed time series are often non-Gaussian, and may follow distributions with a diversity of behaviors concerning the support, the symmetry or the tail properties. Given a correlated Gaussian time series, it is always possible to transform it into a time series with a different distribution, but the question is how this transformation affects the behavior of the autocorrelation function. Here, we study analytically and numerically how the Pearson’s cor- relation of two Gaussian variables changes when the variables are transformed to follow a different destination distribution. Specifically, we consider bounded and unbounded distributions, symmetric and non-symmetric distributions, and distributions with different tail properties, from decays faster than exponential to heavy tail cases including power-laws, and we find how these properties affect the correlation of the final variables. We extend these results to Gaussian time series which are transformed to have a different marginal distribution, and show how the autocorrelation function of the final non-Gaussian time series depends on the Gaussian correlations and on the final marginal distribution. 2024-02-06T13:01:32Z 2024-02-06T13:01:32Z 2019 2020-08-21 journal article https://hdl.handle.net/10630/29925 10.1063/5.0013986 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional American Institute of Physics