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

TL;DR

This paper introduces a novel method combining wavelet analysis and non-stationary surrogates to detect transient spatial correlation patterns in complex, evolving time series from real-world systems.

Contribution

It presents a new approach that preserves amplitude and time-frequency properties of data, improving detection of short-lived spatial coherence over standard methods.

Findings

Effective detection of transient spatial patterns in synthetic data

Application to real-world data reveals meaningful spatial correlations

Method outperforms traditional stationary surrogate-based techniques

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 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.