Time series measured from real spatially extended 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  spatial coherent patterns in non- stationary multivariate time-series. In contrast with classical methods, the surrogate data used here are realisations of a non-stationary linear stochastic process, preserving both the amplitude and time-frequency distributions of original data . These surrogate data are used in combination with an extension of the wavelet coherence to multivariate signals to detect short-lived coherent spatial patterns . 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.
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 multivari- ate 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.
R
real spatially extended
real-world
A
short-lived
R
in non- stationary multivariate
from multivari- ate
R
classical
standard
D
linear
D
. These surrogate data are used in combination with an extension of the wavelet coherence to multivariate signals to detect short-lived coherent spatial patterns