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Recent research demonstrate that prediction of time series by recurrentneural networks (RNNs) based on the noisy input generates a smooth anticipatedtrajectory. We examine the internal dynamics of RNNs and establish a set ofconditions required for such behavior. Based on this analysis we propose a newapproximate algorithm and show that it significantly speeds up the predictiveprocess without loss of accuracy.

A fast noise filtering algorithm for time series prediction using recurrent neural networks