LGC - Local Granger Causality (and Information Storage)
Toolbox for the computation of local Information Dynamics
(Granger causality, Information Storage)
from bivariate time series
Granger causality (GC) is a statistical notion of causal influence based on prediction via linear vector
autoregression. For Gaussian variables it is equivalent to transfer
entropy, an information-theoretic measure of time-directed information
transfer between jointly dependent processes. We exploit such
equivalence and calculate exactly the local Granger causality, i.e.,
the profile of the information transferred from the driver to the
target process at each discrete time point; in this frame, GC is the
average of its local version. The same rationale is exploited to
calculate the local self-fredictability of a process, which is the
linear counterpart of the local Information Storage. Our approach
offers a robust and computationally fast method to follow the
information transfer along the time history of linear stochastic
processes, as well as of nonlinear complex systems studied in the
Gaussian approximation.
DOWNLOAD: Zip file with all scripts and functions: LGC.zip
The code is provided free of charge. It is neither exhaustively tested
nor particularly well documented. The authors accept no liability for
its use. Use, modification and redistribution of the code is allowed in
any way users see fit. Authors ask only that authorship is acknowledged
and ref. [1] is cited upon utilization of the code in integral or
partial form.
DISCLAIMER OF
WARRANTIES AND LIMITATION OF LIABILITY The code is supplied as is and
all use is at your own risk. The authors disclaim all warranties of any
kind, either express or implied, as to the softwares, including, but
not limited to, implied warranties of fitness for a particular purpose,
merchantability or non - infringement of proprietary rights. Neither
this agreement nor any documentation furnished under it is intended to
express or imply any warranty that the operation of the software will
be error - free. Under no circumstances shall the authors of the
softwares provided here be liable to any user for direct, indirect,
incidental, consequential, special, or exemplary damages, arising from
the software, or user' s use or misuse of the softwares. Such
limitation of liability shall apply whether the damages arise from the
use or misuse of the data provided or errors of the software.
Reference:
[1] S Stramaglia, T Scagliarini, Y Antonacci, L Faes, 'Local Granger Causality', Physical Review E Letters, 2021; 103: L020102