6533b7d2fe1ef96bd125f4df
RESEARCH PRODUCT
Local Granger causality
Luca FaesYuri AntonacciTomas ScagliariniSebastiano Stramagliasubject
FOS: Computer and information sciencesInformation transferGaussianFOS: Physical sciencestechniques; information theory; granger causalityMachine Learning (stat.ML)Quantitative Biology - Quantitative Methods01 natural sciences010305 fluids & plasmasVector autoregressionsymbols.namesakegranger causalityGranger causalityStatistics - Machine Learning0103 physical sciencesApplied mathematicstime serie010306 general physicsQuantitative Methods (q-bio.QM)Mathematicsinformation theoryStochastic processDisordered Systems and Neural Networks (cond-mat.dis-nn)Condensed Matter - Disordered Systems and Neural NetworksComputational Physics (physics.comp-ph)Discrete time and continuous timeAutoregressive modelFOS: Biological sciencesSettore ING-INF/06 - Bioingegneria Elettronica E InformaticasymbolsTransfer entropytechniquesPhysics - Computational Physicsdescription
Granger causality is a statistical notion of causal influence based on prediction via 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 transfer at each discrete time point in Gaussian processes; in this frame Granger causality is the average of its local version. 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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2021-01-01 |