Influence analysis for high-dimensional time series with an application to epileptic seizure onset zone detection.
Granger causality is a useful concept for studying causal relations in networks. However, numerical problems occur when applying the corresponding methodology to high-dimensional time series showing co-movement, e.g. EEG recordings or economic data. In order to deal with these shortcomings, we propose a novel method for the causal analysis of such multivariate time series based on Granger causality and factor models. We present the theoretical background, successfully assess our methodology with the help of simulated data and show a potential application in EEG analysis of epileptic seizures.
Institute for Mathematical Methods in Economics, Vienna University of Technology, Vienna, Austria. christoph.flamm@tuwien.ac.at
Christoph
C
Flamm
Andreas
A
Graef
Susanne
S
Pirker
Christoph
C
Baumgartner
Manfred
M
Deistler
D000328:Adult; D001921:Brain / Q000503:physiopathology* / Q000648:ultrastructure; D015984:Causality; D004569:Electroencephalography / Q000706:statistics & numerical data*; D004828:Epilepsies, Partial / Q000503:physiopathology*; D005544:Forecasting; D006801:Humans; D008279:Magnetic Resonance Imaging; D008297:Male; D008959:Models, Neurological; D015999:Multivariate Analysis; D025341:Principal Component Analysis; D013269:Stochastic Processes; D013997:Time Factors