Asian Journal of Information Technology
ISSN: Online 1993-5994ISSN: Print 1682-3915
Abstract
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.
How to cite this article:
Andreas Galka , Heiko Hansen , Tohru Ozaki and Gerd Pfister . Quantification of Nonstationary Structure in High-dimensional Time Series.
DOI: https://doi.org/10.36478/ajit.2004.1165.1172
URL: https://www.makhillpublications.co/view-article/1682-3915/ajit.2004.1165.1172