On data-driven computation of information transfer for causal inference in discrete-time dynamical systems

DOI10.1007/S00332-020-09620-1zbMath1446.37070arXiv1803.08558OpenAlexW3009394821WikidataQ130552132 ScholiaQ130552132MaRDI QIDQ2190698

Yanyan Li

Publication date: 21 June 2020

Published in: Journal of Nonlinear Science (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1803.08558

zbMATH Keywords

time seriesPerron-Frobenius operatordynamical systemcausal inferenceoperator theoryKoopman operator

Mathematics Subject Classification ID

Time series analysis of dynamical systems (37M10) Linear composition operators (47B33) Applications of operator theory in systems, signals, circuits, and control theory (47N70) Computational methods for ergodic theory (approximation of invariant measures, computation of Lyapunov exponents, entropy, etc.) (37M25) Functional analytic techniques in dynamical systems; zeta functions, (Ruelle-Frobenius) transfer operators, etc. (37C30) Numerical problems in dynamical systems (65P99)

Related Items (1)

Data-driven operator theoretic methods for phase space learning and analysis

Uses Software

• TETRAD

Cites Work

• Unnamed Item
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• A Distributional Interpretation of Robust Optimization
• Dynamic mode decomposition of numerical and experimental data
• Time scale modeling of sparse dynamic networks
• Koopman Operator Family Spectrum for Nonautonomous Systems
• Investigating Causal Relations by Econometric Models and Cross-spectral Methods
• On the Problem of Reconstructing an Unknown Topology via Locality Properties of the Wiener Filter

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