基于并行双通道时空网络的流量数据修复技术

Traffic Data Repair Technology Based on Parallel Dual-channel Spatio-temporal Network

流量数据丢失是网络系统中常见的问题,通常由传感器故障、传输错误和存储丢失引起.现有的数据修复方法无法学习流量数据的多维特征,因此本文提出了一种结合双向长短期记忆网络与多尺度卷积网络的双通道并行架构(ST-MFCN)用于填补流量数据的缺失值,同时设计了一种新的对抗性损失函数进一步提高预测精度,该模型有效地学习流量数据的时间特征和动态空间特征.本文在Web traffic time series数据集上对模型进行测试,并与现有的修复方法进行对比,实验结果表明,ST-MFCN能够减少数据恢复的误差,提升了数据修复的精确度,为网络系统中的流量数据修复提供了一种稳健高效的解决方案.

Traffic data loss is common in network systems and is usually caused by sensor failure,transmission errors,and storage loss.The existing data repair methods cannot learn the multi-dimensional characteristics of traffic data.Therefore,this study proposes a dual-channel parallel architecture that combines bidirectional long short-term memory(LSTM)networks with multi-scale convolutional networks(ST-MFCN)for filling the missing values in traffic data.Meanwhile,a novel adversarial loss function is designed to further improve the prediction accuracy,which allows the model to effectively learn the temporal and dynamic spatial features of traffic data.Additionally,the model is tested on the Web traffic time series dataset and compared with the existing repair methods.Experimental results demonstrate that ST-MFCN can reduce data recovery errors and improve data repair accuracy,providing a robust and efficient solution for traffic data repair in network systems.