Quantification of Nonstationary Structure in High-dimensional Time Series

We consider the problem of detecting and quantifying nonstationary structure in time series from highdimensional dynamical systems. This problem is relevant in particular for EEG monitoring, e.g. for the prediction of epileptic seizures, but also for practical data analysis in many other fields. Three groups of measures of nonstationarity are discussed: Correlation dimension, measures based on autoregressive modelling and cross-prediction, and measures based on entropies defined in the spectral or wavelet domains. Results both for simulated and clinical time series are shown.