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- W4285814364 abstract "Network traffic is non-linear and non-stationary in nature, which is difficult to predict. In this paper, to solve this problem, a network traffic prediction method based on the combined PSO-VMD-BiLSTM model is proposed. Firstly, the parameters of the variational modal decomposition (VMD) model are optimized by the particle swarm optimization (PSO) model. Secondly, the PSO-VMD model decomposes and reconstructs the network traffic data to reduce the noise in the original traffic data. Then the bi-directional long and short-term memory (BiLSTM) model is used to predict the reconstructed high-frequency, mid-frequency, and low-frequency sequences, respectively. Finally, the predicted values are summed up. This paper demonstrates that the combined PSO-VMD-BiLSTM model has good adaptability and prediction effect through simulations and comparative experiments on actual network traffic data." @default.
- W4285814364 created "2022-07-19" @default.
- W4285814364 creator A5010635933 @default.
- W4285814364 date "2022-05-20" @default.
- W4285814364 modified "2023-09-27" @default.
- W4285814364 title "A prediction model of Network traffic noise reduction based on PSO-VMD and BiLSTM" @default.
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- W4285814364 doi "https://doi.org/10.1109/cvidliccea56201.2022.9824075" @default.
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