  Reconstruction of a dynamical system from a time series requires the
selection of two parameters, the embedding dimension $d_e$ and the embedding
lag $\tau$. Many competing criteria to select these parameters exist, and all
are heuristic. Within the context of modeling the evolution operator of the
underlying dynamical system, we show that one only need be concerned with the
product $d_e\tau$. We introduce an information theoretic criteria for the
optimal selection of the embedding window $d_w=d_e\tau$. For infinitely long
time series this method is equivalent to selecting the embedding lag that
minimises the nonlinear model prediction error. For short and noisy time series
we find that the results of this new algorithm are data dependent and superior
to estimation of embedding parameters with the standard techniques.
