Measuring information transfer.
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An information theoretic measure is derived that quantifies the statistical coherence between systems evolving in time and is able to distinguish effectively driving and responding elements and to detect asymmetry in the interaction of subsystems.Abstract:
An information theoretic measure is derived that quantifies the statistical coherence between systems evolving in time. The standard time delayed mutual information fails to distinguish information that is actually exchanged from shared information due to common history and input signals. In our new approach, these influences are excluded by appropriate conditioning of transition probabilities. The resulting transfer entropy is able to distinguish effectively driving and responding elements and to detect asymmetry in the interaction of subsystems.read more
Citations
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Recurrence plots for the analysis of complex systems
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Network structure of cerebral cortex shapes functional connectivity on multiple time scales.
TL;DR: Simulating nonlinear neuronal dynamics on a network that captures the large-scale interregional connections of macaque neocortex, and applying information theoretic measures to identify functional networks, this work finds structure–function relations at multiple temporal scales.
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Detecting Causality in Complex Ecosystems
George Sugihara,Robert M. May,Hao Ye,Chih-hao Hsieh,Ethan R. Deyle,Michael J. Fogarty,Stephan B. Munch +6 more
TL;DR: A new method, based on nonlinear state space reconstruction, that can distinguish causality from correlation is introduced, and extends to nonseparable weakly connected dynamic systems (cases not covered by the current Granger causality paradigm).
Nonlinear Time Series Analysis.
TL;DR: This thesis applies neural network feature selection techniques to multivariate time series data to improve prediction of a target time series and results indicate that the Stochastics and RSI indicators result in better prediction results than the moving averages.
Journal ArticleDOI
Nonlinear dynamical analysis of EEG and MEG: Review of an emerging field
TL;DR: Interpretation of results in terms of 'functional sources' and 'functional networks' allows the identification of three basic patterns of brain dynamics: normal, ongoing dynamics during a no-task, resting state in healthy subjects, and hypersynchronous, highly nonlinear dynamics of epileptic seizures and degenerative encephalopathies.
References
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Book
Nonlinear time series analysis
Holger Kantz,Thomas Schreiber +1 more
TL;DR: Using nonlinear methods when determinism is weak, as well as selected nonlinear phenomena, is suggested to be a viable alternative to linear methods.
Nonlinear Time Series Analysis.
TL;DR: This thesis applies neural network feature selection techniques to multivariate time series data to improve prediction of a target time series and results indicate that the Stochastics and RSI indicators result in better prediction results than the moving averages.
Book
Time Series Prediction: Forecasting The Future And Understanding The Past
TL;DR: By reading time series prediction forecasting the future and understanding the past, you can take more advantages with limited budget.