A fast noise filtering algorithm for time series prediction using recurrent neural networks
arXiv:2007.08063
Abstract
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.
15 pages, 10 figures; typos corrected; the notation table removed; an appendix added