Exo-LSTM: traffic flow prediction based on multifractal wavelet theory

作     者：Yang Fan Jiang Mengya Yang Fan;Jiang Mengya

作者机构：State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijing 100876China Purple Mountain LaboratoriesNanjing 211111China 

出 版 物：《The Journal of China Universities of Posts and Telecommunications》 (中国邮电高校学报（英文版）)

年 卷 期：2021年第28卷第5期

页      面：102-110页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 082303[工学-交通运输规划与管理] 0835[工学-软件工程] 082302[工学-交通信息工程及控制] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0823[工学-交通运输工程] 

基　　金：supported by the National Key Research and Development Program of China (2018YFB180060) the Science and Technology on Information Transmission and Dissemination in Communication Networks Laboratory Research Project (SKX192010028) 

主　　题：long-short term memory(LSTM) exogenous sequences multifractal wavelet model 

摘      要：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.