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Neural Granger Causality. [PDF]
IEEE Trans Pattern Anal Mach Intell, 2022 While most classical approaches to Granger causality detection assume linear dynamics, many interactions in real-world applications, like neuroscience and genomics, are inherently nonlinear.
Tank A +4 more
europepmc +6 more sources
Local Granger causality. [PDF]
Physical Review E, 2020 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 ...
S. Stramaglia +3 more
semanticscholar +6 more sources
Granger Causality: A Review and Recent Advances. [PDF]
Annu Rev Stat Appl, 2022 Introduced more than a half-century ago, Granger causality has become a popular tool for analyzing time series data in many application domains, from economics and finance to genomics and neuroscience.
Shojaie A, Fox EB.
europepmc +3 more sources
Bibliometric Analysis of Granger Causality Studies [PDF]
Entropy, 2023 Granger causality provides a framework that uses predictability to identify causation between time series variables. This is important to policymakers for effective policy management and recommendations.
Weng Siew Lam +3 more
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Evaluation of Granger Causality Measures for Constructing Networks from Multivariate Time Series [PDF]
Entropy, 2019 Granger causality and variants of this concept allow the study of complex dynamical systems as networks constructed from multivariate time series.
Elsa Siggiridou +3 more
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The MVGC multivariate Granger causality toolbox: a new approach to Granger-causal inference. [PDF]
Journal of Neuroscience Methods, 2014 Wiener-Granger causality ("G-causality") is a statistical notion of causality applicable to time series data, whereby cause precedes, and helps predict, effect. It is defined in both time and frequency domains, and allows for the conditioning out of common causal influences.
L. Barnett, A. Seth
semanticscholar +4 more sources
Granger Causality Analysis of Chignolin Folding. [PDF]
J Chem Theory Comput, 2022 Constantly advancing computer simulations of biomolecules provide huge amounts of data that are difficult to interpret. In particular, obtaining insights into functional aspects of macromolecular dynamics, often related to cascades of transient events, calls for methodologies that depart from the well-grounded framework of equilibrium statistical ...
Sobieraj M, Setny P.
europepmc +3 more sources
Testing for Granger non-causality in heterogeneous panels
Economic Modelling, 2012 Elena Dumitrescu, Christophe Hurlin
exaly +2 more sources
Frontiers in Computational Neuroscience, 2021 In recent years, affective computing based on electroencephalogram (EEG) data has attracted increased attention. As a classic EEG feature extraction model, Granger causality analysis has been widely used in emotion classification models, which construct ...
Dongwei Chen +5 more
doaj +1 more source
Renewable energy, oil prices, and economic activity: A Granger-causality in quantiles analysis
Energy Economics, 2018 Victor Emilio Troster +2 more
exaly +2 more sources