Non-uniform state space reconstruction and coupling detection

Abstract

We investigate the state space reconstruction from multiple time series derived from continuous and discrete systems and propose a method for building embedding vectors progressively using information measure criteria regarding past, current and future states. The embedding scheme can be adapted for different purposes, such as mixed modelling, cross-prediction and Granger causality. In particular we apply this method in order to detect and evaluate information transfer in coupled systems. As a practical application, we investigate in records of scalp epileptic EEG the information flow across brain areas.

21 pages, 11 figures, to be published in Physical Review E

References in corpus (3)

• Multivariate phase space reconstruction by nearest neighbor embedding with different time delays
• Transition from phase to generalized synchronization in time-delay systems
• Nearest neighbor embedding with different time delays

Cited by in corpus (28)

• Direct coupling information measure from non-uniform embedding
• Evolving networks in the human epileptic brain
• Granger Causality in Multi-variate Time Series using a Time Ordered Restricted Vector Autoregressive Model
• Large-scale directed network inference with multivariate transfer entropy and hierarchical statistical testing
• Multiscale Information Decomposition: Exact Computation for Multivariate Gaussian Processes
• Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition
• Partial Transfer Entropy on Rank Vectors
• Inferring network properties from time series using transfer entropy and mutual information: validation of multivariate versus bivariate approaches
• Evaluation of Granger causality measures for constructing networks from multivariate time series
• A unified and automated approach to attractor reconstruction
• Editorial Comment on the Special Issue of "Information in Dynamical Systems and Complex Systems"
• Identifying Coupling Structure in Complex Systems through the Optimal Causation Entropy Principle
• Causation entropy from symbolic representations of dynamical systems
• The causal inference of cortical neural networks during music improvisations
• Identifying delayed directional couplings with symbolic transfer entropy
• Detecting causality in multivariate time series via non-uniform embedding
• Causality indices for bivariate time series data: a comparative review of performance
• Time-series-analysis-based detection of critical transitions in real-world non-autonomous systems
• Variational Embedding Multiscale Sample Entropy:complexity-based analysis for multichannel systems
• Space-time nature of causality
• Inference of topology and the nature of synapses, and the flow of information in neuronal networks
• Non-Asymptotic Guarantees for Reliable Identification of Granger Causality via the LASSO
• Recurrence flow measure of nonlinear dependence
• Sparse model from optimal nonuniform embedding of time series
• A high dimensional delay selection for the reconstruction of proper Phase Space with Cross auto-correlation
• Entropic Regression DMD (ERDMD) Discovers Informative Sparse and Nonuniformly Time Delayed Models
• Extension of causal decomposition in the mutual complex dynamic process
• Effective Connectivity-Based Neural Decoding: A Causal Interaction-Driven Approach