A technique to forecast spatiotemporal time series is presented.
it uses a Proper Ortogonal or Karhunen-Loeve 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 onedimensional complex Ginzburg-Landau equation
in a finite domain.
Also available from LANL preprint server as paper nlin/0003060.