Seasonal footprints on ecological time series and jumps in dynamic states of protein configurations from a nonlinear forecasting method characterization
Year: 2025
Type: article
Abstract: We have analyzed phenology data and protein configurations from molecular dynamics simulations with the nonlinear forecasting method proposed by May and Sugihara. Our primary focus in this work is to characterize the dynamic state of a system by quantifying prediction quality from data. Full plots of prediction quality as a function of dimensionality E and forecasting time Tp , the two basic parameters of the method, give fast and valuable information about complex systems dynamics. We detect changes in protein dynamics due to mutations and, regarding ecology data, we show how cycles and rhythms of the environment manifests in parameter space ( E , T p ) for some species.
Source: Journal of Physics Complexity
Institution: Instituto Venezolano de Investigaciones Científicas
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,600
OpenAlex ID: W4408320199