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

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 nonstationary 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.