  When building linear or nonlinear models one is faced with the problem of
selecting the best set of variable with which to predict the future dynamics.
In nonlinear time series analysis the problem is to select the correct time
delays in the time delay embedding. We propose a new technique which can
quantify the suitability of a particular set of variables and we suggests a
computationally efficient scheme to determine the best non-uniform time delay
embedding for modeling of time series. Our results are based on the assumption
that, in general, the variables which give the best local constant model will
also give the best nonlinear model. In a wide variety of experimental and
simulated systems we find that this method produces dynamics that are more
realistic and predictions that are more accurate than standard uniform
embeddings.
