NeuroKoopman Dynamic Causal Discovery

arXiv:2404.16326v1 Announce Type: new
Abstract: In many real-world applications where the system dynamics has an underlying interdependency among its variables (such as power grid, economics, neuroscience, omics networks, environmental ecosystems, and others), one is often interested in knowing whether the past values of one time series influences the future of another, known as Granger causality, and the associated underlying dynamics. This paper introduces a Koopman-inspired framework that leverages neural networks for data-driven learning of the Koopman bases, termed NeuroKoopman Dynamic …

abstract applications arxiv causal causality cs.lg discovery dynamic dynamics economics ecosystems environmental future grid networks neuroscience power series time series type values variables world