A realistic network traffic forecasting method based on VMD and LSTM network

Global internet usage is growing dramatically in recent years, making it difficult for operators to allocate resources rationally and maintain network security. If the network traffic can be accurately and timely forecasted, it can help operators relieve this pressure. However, due to the non-linearity and non-stationarity of network traffic, it's difficult to capture dynamic characteristics to obtain excellent prediction results for large number of traditional methods. To address this issue, we propose a novel long short-term memory (LSTM) neural network combined with variational mode decomposition (VMD) for network traffic forecasting. It firstly applies VMD to decompose the time series according to frequency information, extracting nonlinear and non-stationary features in the sequence, and then LSTM exploited to capture the long-term dependencies of network traffic data. We combine the decomposition algorithm with LSTM to establish a mapping between historical network traffic data and future ones. Experimental results based on real-world datasets demonstrate that the prediction accuracy of our model overperforms the existing state-of-the-art algorithms,