Multi-level stochastic refinement for complex time series and fields: A Data-Driven Approach
M. Sinhuber,
J. Friedrich,
R. Grauer
et al.
Abstract:Spatio-temporally extended nonlinear systems often exhibit a remarkable complexity in space and time. In many cases, extensive datasets of such systems are difficult to obtain, yet needed for a range of applications. Here, we present a method to generate synthetic time series or fields that reproduce statistical multi-scale features of complex systems. The method is based on a hierarchical refinement employing transition probability density functions (PDFs) from one scale to another. We address the case in whi… Show more
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