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所属单位：自动化科学与电气工程学院
发表刊物：IEEE Transactions on Automation Science and Engineering
刊物所在地：美国
关键字：Long short-term memory (LSTM), machine learning, network traffic prediction, Savitzky-Golay (SG)
摘要：Accurate and real-time prediction of network traffic can not only help system operators allocate resources rationally according to their actual business needs but also help them assess the performance of a network and analyze its health status. In recent years, neural networks have been proved suitable to predict time series data, represented by the model of a long short-term memory (LSTM) neural network and a temporal convolutional network (TCN). This article proposes a novel hybrid prediction method named SG and TCN-based LSTM (ST-LSTM) for such network traffic prediction, which synergistica
论文类型：基础研究
论文编号：DOI: 10.1109/TASE.2021.3077537
一级学科：计算机科学与技术
文献类型：期刊
卷号：PP
期号：99
页面范围：1-11
ISSN号：1545-5955
是否译文：否
CN号：null
发表时间：2021-05-21
收录刊物：SCI
发布期刊链接：https://ieeexplore-ieee-org-s.vpn.buaa.edu.cn:8118/document/9439149