  Recent research demonstrate that prediction of time series by recurrent
neural networks (RNNs) based on the noisy input generates a smooth anticipated
trajectory. We examine the internal dynamics of RNNs and establish a set of
conditions required for such behavior. Based on this analysis we propose a new
approximate algorithm and show that it significantly speeds up the predictive
process without loss of accuracy.
