Modeling Multivariate Covariance Nonstationary Time Series and Their Dependency Structure: An Application to Human Epileptic Event EEG (Electrode Electroencephalogram) Analysis.
Abstract
The parametric modeling of covariance nonstationary time series and the computation of their changing interdependency structure from the fitted model are treated. The nonstationary time series are modeled by a multivariate time varying autoregressive (AR) model. The time evolution of the AR parameters is expressed as linear combinations of discrete Legendre orthogonal polynomial functions of time. The model is fitted by a Householder transformation-Akaike AIC (AR order-polynomial degree-regression subset selection) method. The computation of the instantaneous dependence, feedback and causality structure of the time series from the fitted model, is discussed. An example of the modeling and determination of instantaneous causality in a human implanted electrode seizure event Electroencephalogram (EEG) is shown. Keywords: Time-varying; time series; autoregression; feedback; causality; electroencephalogram.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 21, 1987
- Accession Number
- ADA176798
Entities
People
- Will Gersch
Organizations
- Stanford University