title: Forecasting confined spatiotemporal chaos with genetic algorithms.
abstract: A technique to forecast spatiotemporal time series is presented. It uses a proper orthogonal or Karhunen-Loève decomposition to encode large spatiotemporal data sets in a few time series, and genetic algorithms to efficiently extract dynamical rules from the data. The method works very well for confined systems displaying spatiotemporal chaos, as exemplified here by forecasting the evolution of the one-dimensional complex Ginzburg-Landau equation in a finite domain.
authors.affiliation: Instituto Mediterráneo de Estudios Avanzados, IMEDEA (CSIC-Universitat de les Illes Balears), 07071 Palma de Mallorca, Spain.
authors.forename: C
authors.initials: C
authors.lastname: López
authors.forename: A
authors.initials: A
authors.lastname: Alvarez
authors.forename: E
authors.initials: E
authors.lastname: Hernández-García
mesh_terms: D000465:Algorithms; D005823:Genetics / Q000706:statistics & numerical data*; D017711:Nonlinear Dynamics