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Investigating dynamic causal network with unified Granger causality analysis
Journal of Neuroscience Methods, 2023Dynamic coupling phenomena characterize a widespread fundamental mechanism for the functional brain, which involves large-scale interactions at a multi-level. The Granger causality analysis (GCA) provides a data-driven procedure to investigate causal connections and has the potential to be a powerful dynamic capturing tool.In this paper, distinct from ...
Fei, Li +6 more
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The Granger causality analysis of stocks based on clustering
Cluster Computing, 2018In the research of the relationship between stocks, people tend to focus more on using the domestic or foreign indices to study the inter-national, inter-regional and inter-industry relations, but few people analyze and tap the connection between individual stocks directly.
Siyu Bai, Wei Cui, Long Zhang
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Granger Causality—Statistical Analysis Under a Configural Perspective
Integrative Psychological and Behavioral Science, 2013The concept of Granger causality can be used to examine putative causal relations between two series of scores. Based on regression models, it is asked whether one series can be considered the cause for the second series. In this article, we propose extending the pool of methods available for testing hypotheses that are compatible with Granger ...
Von Eye, Alexander +2 more
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Comparison Analysis: Granger Causality and New Causality and Their Applications to Motor Imagery
IEEE Transactions on Neural Networks and Learning Systems, 2016In this paper we first point out a fatal drawback that the widely used Granger causality (GC) needs to estimate the autoregressive model, which is equivalent to taking a series of backward recursive operations which are infeasible in many irreversible chemical reaction models. Thus, new causality (NC) proposed by Hu et al. (2011) is theoretically shown
Sanqing Hu +5 more
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The extended Granger causality analysis for Hodgkin–Huxley neuronal models
Chaos: An Interdisciplinary Journal of Nonlinear Science, 2020How to extract directions of information flow in dynamical systems based on empirical data remains a key challenge. The Granger causality (GC) analysis has been identified as a powerful method to achieve this capability. However, the framework of the GC theory requires that the dynamics of the investigated system can be statistically linearized; i.e ...
Hong Cheng, David Cai, Douglas Zhou
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A MATLAB toolbox for Granger causal connectivity analysis
Journal of Neuroscience Methods, 2010Assessing directed functional connectivity from time series data is a key challenge in neuroscience. One approach to this problem leverages a combination of Granger causality analysis and network theory. This article describes a freely available MATLAB toolbox--'Granger causal connectivity analysis' (GCCA)--which provides a core set of methods for ...
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Telecommunications and Economic Activity: An Analysis of Granger Causality
Journal of Management Information Systems, 2001The pervasive role of telecommunications in contemporary commerce is well documented, and has dramatically increased the demand for services. Across the world, countries are seeking to improve telecommunications infrastructure and benefit from anticipated increases in economic activity, and a causal relation between the two is often tacitly assumed ...
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A Measure of Prediction Precision for Granger Causality Analysis
2021 13th International Conference on Measurement, 2021Identification of causal interactions from time-series data is an emerging issue in many fields of science. The causal connection between two variables is detected by the Granger causality if the prediction of one variable based on a linear combination of its past values can be improved by incorporating past values of another.
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Granger causality analysis in the neural mass model
2015 34th Chinese Control Conference (CCC), 2015The study of brain functional connectivity has become an important aspect of neuroscience. With the development of different methods to detect functional connectivity, the neural mass model based on physiology provides a basis for validating methods. As a popular method of discovering functional connectivity, Granger causality is applied to many fields
Li Liang +7 more
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Causality analysis of neural connectivity: New tool and limitations of spectral Granger causality
Neurocomputing, 2012Granger causality (GC) is one of the most popular measures to reveal causality influence of time series based on the estimated linear regression model and has been widely applied in economics and neuroscience due to its simplicity, understandability and easy implementation.
Sanqing Hu, Hualou Liang
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