Research Article
Research on Website Traffic Prediction Method Based on Deep Learning
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@INPROCEEDINGS{10.1007/978-3-030-97124-3_32, author={Rong Bao and Kailiang Zhang and Jing Huang and Yuxin Li and Weiwei Liu and Likai Wang}, title={Research on Website Traffic Prediction Method Based on Deep Learning}, proceedings={Simulation Tools and Techniques. 13th EAI International Conference, SIMUtools 2021, Virtual Event, November 5-6, 2021, Proceedings}, proceedings_a={SIMUTOOLS}, year={2022}, month={3}, keywords={Network traffic prediction Bidirectional LSTM Deep learning Activation function Automatic polling}, doi={10.1007/978-3-030-97124-3_32} }- Rong Bao
Kailiang Zhang
Jing Huang
Yuxin Li
Weiwei Liu
Likai Wang
Year: 2022
Research on Website Traffic Prediction Method Based on Deep Learning
SIMUTOOLS
Springer
DOI: 10.1007/978-3-030-97124-3_32
Abstract
Accurate prediction of website traffic can improve network management, improve service quality, and improve the end user experience. Using the neural network learning and memory function, we can predict the time series of network traffic flow. Based on short - and long-term memory, we design the structure of data and neural network model and select the nonlinear activation function. The experimental results show that the proposed prediction method obtains the higher accuracy, which can effectively predict the traffic of visiting websites. At the same time, this method can effectively reduce the training time. By accurate traffic prediction, the network manager can adjust scheduling strategy to guarantee the user experience.
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