Detecting dynamic spatial correlation patterns with generalized wavelet coherence and non-stationary surrogate data
Year: 2019
Type: article
Abstract: Time series measured from real-world systems are generally noisy, complex and display statistical properties that evolve continuously over time. Here, we present a method that combines wavelet analysis and non-stationary surrogates to detect short-lived spatial coherent patterns from multivariate time-series. In contrast with standard methods, the surrogate data proposed here are realisations of a non-stationary stochastic process, preserving both the amplitude and time-frequency distributions of original data. We evaluate this framework on synthetic and real-world time series, and we show that it can provide useful insights into the time-resolved structure of spatially extended systems.
Source: Scientific Reports
Authors: Mario Chavez, Bernard Cazelles
Institutions: Centre National de la Recherche Scientifique, Pitié-Salpêtrière Hospital, Institut du Cerveau, École Normale Supérieure - PSL, Institut de Biologie de l'École Normale Supérieure, Unité de Modélisation Mathématique et Informatique des Systèmes Complexes, Institut de Recherche pour le Développement
Language: English
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $1,790
Funder: Agence Nationale de la Recherche
OpenAlex ID: W2783582467