An intelligent network traffic prediction method based on Butterworth filter and CNN–LSTM

Abstract

Accurate and real-time network traffic prediction is of paramount importance in the fields of network management, performance optimization, and fault diagnosis. It provides strong support for autonomous network control, network administration and network services. Therefore, we propose a novel approach for network traffic prediction, which integrates the Butterworth filter, Convolutional Neural Network and Long Short-Term Memory network(BWCL). First, this method the network traffic data to frequency domain processing, utilizing the Butterworth filter to extract its low-frequency component. The residual component is generated by subtracting the low-frequency component from the network traffic sequence. Then, CNN–LSTM prediction models are employed to capture the spatial and temporal features of the data in different frequency bands. Finally, the prediction results of the two models are linearly summed to represent the final prediction value. To validate the feasibility of the proposed model, we construct a variety of datasets with statistical features by taking the raw network traffic in single\\multi-cell scenarios at two different temporal granularities: minutes and hours. In the Pytorch experimental environment, we evaluate the performance of the model using MSE, RMSE, MAE, and R2 performance metrics. The experimental results show that the prediction accuracy of the model is improved by 25% compared to the existing time series prediction models. This innovative approach provides new ideas in the field of time series forecasting, which has a broad application prospect.