Network Traffic Prediction Method Based on Time Series Characteristics

With the continuous development of computer networks in recent years, the scale and types of services carried by the network are increasing. Accurate traffic prediction results provide the main support and reference basis for network operation and maintenance functions such as network attack detection. Since network traffic has certain dynamics, continuity, and long correlation and self-similar characteristics, the artificial intelligence method is generally used for network traffic prediction. Among them, the recurrent neural network has a short-term memory performance and has a good prediction effect for time series data such as network traffic. However, when the time series span is relatively long, the problem of gradient disappearance or gradient explosion may occur, so further optimization is required. In this paper, we propose a network traffic prediction method based on parameter pre-training of clockwork neural network. This method is first based on CW-RNN, and then introduces the differential evolution algorithm to pre-train the clock parameters. At the same time, the differential evolution algorithm is further improved by changing its crossover factor and mutation factor to improve the accuracy of its convergence. Through computer simulation, the flow prediction method proposed in this paper can obtain accurate prediction results.