论文标题
使用复发性神经网络的时间序列预测的快速噪声过滤算法
A fast noise filtering algorithm for time series prediction using recurrent neural networks
论文作者
论文摘要
最近的研究表明，基于嘈杂输入的复发神经网络（RNN）对时间序列的预测会产生平稳的预期轨迹。我们检查了RNN的内部动力学，并建立了此类行为所需的一组条件。基于此分析，我们提出了一种新的近似算法，并表明它可以显着加快预测过程而不会丧失准确性。
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.
