  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.
