%A Tanizawa,Toshihiro
%A Nakamura,Tomomichi
%D 2022
%J Frontiers in Network Physiology
%C
%F
%G English
%K multivariate time series,Statistical Modeling,transfer entropy,Model selection,Auto-regressive models
%Q
%R 10.3389/fnetp.2022.943239
%W
%L
%M
%P
%7
%8 2022-October-07
%9 Original Research
%#
%! Detecting relationships using RAR modeling
%*
%<
%T Detecting the relationships among multivariate time series using reduced auto-regressive modeling
%U https://www.frontiersin.org/articles/10.3389/fnetp.2022.943239
%V 2
%0 JOURNAL ARTICLE
%@ 2674-0109
%X An information theoretic reduction of auto-regressive modeling called the Reduced Auto-Regressive (RAR) modeling is applied to several multivariate time series as a method to detect the relationships among the components in the time series. The results are compared with the results of the transfer entropy, one of the common techniques for detecting causal relationships. These common techniques are pairwise by definition and could be inappropriate in detecting the relationships in highly complicated dynamical systems. When the relationships between the dynamics of the components are linear and the time scales in the fluctuations of each component are in the same order of magnitude, the results of the RAR model and the transfer entropy are consistent. When the time series contain components that have large differences in the amplitude and the time scales of fluctuation, however, the transfer entropy fails to detect the correct relationships between the components, while the results of the RAR modeling are still correct. For a highly complicated dynamics such as human brain activity observed by electroencephalography measurements, the results of the transfer entropy are drastically different from those of the RAR modeling.