Using reservoir computing in a decomposition approach for time series prediction
Using reservoir computing in a decomposition approach for time series prediction
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在时间序列预测的分解方法中使用储层计算
DOI：
10.1016/j.micpro.2016.03.009
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发表时间：
2008
期刊：
影响因子：
--
通讯作者：
D. Stroobandt
中科院分区：
文献类型：
--
作者：
F. Wyffels;B. Schrauwen;D. Stroobandt
在本文中，我们结合联合收割机小波分解和递归神经网络提供快速和准确的时间序列预测。利用小波分解将原始时间序列分解为一个更易于预测的时间序列层次。我们的解决方案的预测核心是由水库计算，这是一个最近开发的技术非常快速的训练递归神经网络。2008年ESTSP竞赛的三个时间序列将被用作我们的方法的说明。
In this paper we combine wavelet decomposition and recurrent neural networks to provide fast and accurate time series predictions. The original time series is decomposed by means of wavelet decomposition into a hierarchy of time series which are easier to predict. The prediction core of our solution is given by reservoir computing, which is a recently developed technique for the very fast training of recurrent neural networks. The three time series of the ESTSP 2008 competition will be used as an illustration for our method.