%0 Journal Article
%A 杨帆
%A 姜梦雅
%A 
%T <div align="left">
	Exo-LSTM: traffic flow prediction based on multifractal wavelet theory
</div>
%D 2021
%R 10.19682/j.cnki.1005-8885.2021.0027
%J Journal of China Universities of Posts and Telecommunications
%P 102-110
%V 28
%N 5
%X <p align="justify">
	In order to predict traffic flow more accurately and 
improve network performance, based on the multifractal wavelet theory, a
 new traffic prediction model named exo-LSTM is proposed. Exo represents
 exogenous sequence used to provide a detailed sequence for the model, 
LSTM represents long short-term memory used to predict unstable traffic 
flow. Applying multifractal traffic flow to the exo-LSTM model and other
 existing models, the experiment result proves that exo-LSTM prediction 
model achieves better prediction accuracy.
</p>
%U https://jcupt.bupt.edu.cn/EN/10.19682/j.cnki.1005-8885.2021.0027