- 12-12-2018 14:00 |
- Aula 8, 1er piso, Pab. I.
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.