TY  - JOUR
T1  - Quantification of Nonstationary Structure in High-dimensional Time Series
AU - , Andreas Galka AU - , Heiko Hansen AU - , Tohru Ozaki AU - , Gerd Pfister 
JO  - Asian Journal of Information Technology
VL  - 3
IS  - 12
SP  - 1165
EP  - 1172
PY  - 2004
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2004.1165.1172
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2004.1165.1172
KW  - 
AB  - We consider the problem of detecting and quantifying nonstationary structure in time series from high-dimensional 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.
ER  -