Making sense of a complex world [chaotic events modeling]

Addresses the identification of nonlinear systems from output time series, which we have called dynamic modeling. We start by providing the mathematical basis for dynamic modeling and show that it is equivalent to a multivariate nonlinear prediction problem in the reconstructed space. We address the importance of dynamic reconstruction for dynamic modeling. Recognizing that dynamic reconstruction is an ill-defined inverse problem, we describe a …