2004/05/03 by Nicola Ancona, Daniele Marinazzo, Ancona, Nicola +3
Biochemistry, Genetics and Molecular Biology · Computer Science · Physics and Astronomy · #Chaos control and synchronization #Data Analysis #FOS: Biological sciences #FOS: Physical sciences #Fractal and DNA sequence analysis #Medical Physics (physics.med-ph) #Neural Networks and Applications #Quantitative Methods (q-bio.QM) #Statistics and Probability (physics.data-an) #physics.data-an #physics.med-ph #q-bio.QM
paper · pdf · doi:10.48550/arxiv.physics/0405009
8 pages, 4 figures
arxiv created 2004/05/03 · openalex publication_date 2004/05/03 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We consider extension of Granger causality to nonlinear bivariate time series. In this frame, if the prediction error of the first time series is reduced by including measurements from the second time series, then the second time series is said to have a causal influence on the first one. Not all the nonlinear prediction schemes are suitable to evaluate causality, indeed not all of them allow to quantify how much the knowledge of the other time series counts to improve prediction error. We present a novel approach with bivariate time series modelled by a generalization of radial basis functions and show its application to a pair of unidirectionally coupled chaotic maps and to a physiological example.