Modeling multivariate covariance nonstationary time series and their dependency structure
Technical Report
·
OSTI ID:5569247
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-AIC order determination, 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 EEG is shown.
- Research Organization:
- Lawrence Livermore National Lab., CA (USA)
- DOE Contract Number:
- W-7405-ENG-48
- OSTI ID:
- 5569247
- Report Number(s):
- UCID-20625; ON: DE86010052
- Country of Publication:
- United States
- Language:
- English
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