MARIO CHAVEZ - Detecting dynamic spatial correlation with generalized wavelet coherence and non-stationary surrogate data

Mario Chavez.

CNRS UMR-7225, Hôpital Pitié-Salpêtrière, Paris, France.

VIERNES 14/12/2018, 14 hs. 

Aula Seminario, 2do piso, Pab. I. 

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 used 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.


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