Existing Nonlinear dimensionality reduction (NLDR) algorithms make the assumption that distances between observations are uniformly scaled. Unfortunately, with many interesting systems, this assumption does not hold. We present a new technique called Temporal NLDR (TNLDR), which is specifically designed for analyzing the high-dimensional observations obtained from random-walks with dynamical systems that have external controls. It uses the additional information implicit in ordered sequences of observations to compensate for non-uniform scaling in observation space. We demonstrate that TNLDR computes more accurate estimates of intrinsic state than regular NLDR, and we show that accurate estimates of state can be used to train accurate models of dynamical systems.