Enhancing the accuracy of a data-driven reconstruction of bivariate jump-diffusion models with corrections for higher orders of the sampling interval
Data Analysis, Statistics and Probability
2021-05-26 v1 Chaotic Dynamics
Abstract
We evaluate the significance of a recently proposed bivariate jump-diffusion model for a data-driven characterization of interactions between complex dynamical systems. For various coupled and non-coupled jump-diffusion processes, we find that the inevitably finite sampling interval of time-series data negatively affects the reconstruction accuracy of higher-order conditional moments that are required to reconstruct the underlying jump-diffusion equations. We derive correction terms for conditional moments in higher orders of the sampling interval and demonstrate their suitability to strongly enhance the data-driven reconstruction accuracy.
Keywords
Cite
@article{arxiv.2101.09728,
title = {Enhancing the accuracy of a data-driven reconstruction of bivariate jump-diffusion models with corrections for higher orders of the sampling interval},
author = {Esra Aslim and Thorsten Rings and Lina Zabawa and Klaus Lehnertz},
journal= {arXiv preprint arXiv:2101.09728},
year = {2021}
}
Comments
17 pages, 4 figures