基于注意力机制的CNN-LSTM模型及其应用OA北大核心CSCDCSTPCD
Attention Mechanism-Based CNN-LSTM Model and Its Application
时序数据存在时序性,并且其短序列的特征存在重要程度差异性.针对时序数据特征,提出一种基于注意力机制的卷积神经网络(CNN)联合长短期记忆网络(LSTM)的神经网络预测模型,融合粗细粒度特征实现准确的时间序列预测.该模型由两部分构成:基于注意力机制的CNN,在标准CNN网络上增加注意力分支,以抽取重要细粒度特征;后端为LSTM,由细粒度特征抽取潜藏时序规律的粗粒度特征.在真实的热电联产供热数据上的实验表明,该模型比差分整合移动平均自回归、支持向量回…查看全部>>
Time series have temporal property, and the characteristics of its short sequences are different in importance. Aiming at the characteristics of time series, a neural network prediction model based on Convolution Neural Network (CNN)and Long Short-Term Memory(LSTM)is proposed, which combines coarse and fine grain features to achieve accurate time series prediction. The model consists of two parts. CNN based on attention mechanism adds attention branch to sta…查看全部>>
LI Mei;NING Dejun;GUO Jiacheng
Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 200120, China 2.University of Chinese Academy of Sciences, Beijing 100049, ChinaShanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 200120, China 2.University of Chinese Academy of Sciences, Beijing 100049, China
计算机与自动化
注意力机制卷积神经网络(CNN)长短期记忆网络(LSTM)时间序列负荷预测
attention mechanism Convolution Neural Network(CNN) Long Short-Term Memory Network(LSTM)time series load forecasting
《计算机工程与应用》 2019 (13)
20-27,8
中国科学院战略性先导科技专项(No.XDA06010800).
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