Abstract: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.