基于混合深度学习的网络流量预测模型研究OA

Aiming at the shortcomings of existing network traffic prediction models,such as single input data,difficult parameter optimization,and low prediction accuracy,a hybrid prediction model was pro-posed,which incorporated variational mode decomposition,a Transformer encoder,and a bidirec-tional gated recurrent unit(BiGRU),and an improved sparrow search algorithm was used to optimize the mod-el parameters.The model extracted multi-scale frequency characteristics of the traffic se-quence and introduced a bidirectional information interaction mechanism between the Transformer encoder and the BiGRU module to further improve the prediction performance through feature feedback and fusion.The experimental results show that the evaluation indicators of the proposed model reduce by more than 60%compared with other methods.In addition,ablation experiments verify the effectiveness of each module in improving prediction accuracy.