计算机技术与发展2017，Vol.27Issue(3)：35-38,43,5.DOI:10.3969/j.issn.1673-629X.2017.03.007
基于深度循环神经网络的时间序列预测模型
A Prediction Model for Time Series Based on Deep Recurrent Neural Network
摘要
Abstract
Aimed at the problems of high-nonlinearity and nondeterminacy for hydrology time series,a prediction model for hydrology time series based on Wavelet Analysis and Deep Recurrent Neural Network ( WA-DRNN) is put forward by using the predictive capabil-ity of deep recurrent neural network,combined with the wavelet analysis for the reconstruction of the original time series and training of high and low frequency series. The network training adopts Back Propagation Through Time ( BPTT) algorithm to update the network weight. The experiment shows that the WA-RNN model is better than the normal DRNN model in the mean square error and absolute er-ror,and for the reason of multiscale the model can decrease the lag of prediction. It turns out the WA-DRNN model has advantages of higher predictive accuracy and less lag,which is helpful for application of hydrology time series prediction of deep learning algorithm.关键词
小波分析/深度循环神经网络/时间序列/预测Key words
wavelet analysis/DRNN/time series/prediction分类
信息技术与安全科学引用本文复制引用
杨祎玥,伏潜,万定生..基于深度循环神经网络的时间序列预测模型[J].计算机技术与发展,2017,27(3):35-38,43,5.基金项目
国家科技支撑计划课题(2015BAB07B01) （2015BAB07B01）
水利部公益性行业科研专项(201501022) （201501022）
