Traffic Forecasting of Back Servers Based on ARIMA-LSTM-CF Hybrid Model

Traffic Forecasting of Back Servers Based on ARIMA-LSTM-CF Hybrid Model

Abstract Accurate server traffic prediction can help enterprises formulate network resource allocation strategies in advance and reduce the probability of network congestion. Traditional prediction models ignore the unique data characteristics of server traffic that can be used to optimize the predi...

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Bibliographic Details
Main Authors: Erzhuang Yao, Lanjie Zhang, Xuehua Li, Xiang Yun
Format: Article
Language:English
Published: Springer 2023-04-01
Series:International Journal of Computational Intelligence Systems
Subjects:
Traffic forecasting
Hybrid model
ARIMA
LSTM
Data characteristics
Online Access:https://doi.org/10.1007/s44196-023-00232-7

Internet

https://doi.org/10.1007/s44196-023-00232-7

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