FARIMA Model Based on Particle Swarm-genetic Hybrid Algorithm Optimization and Application

To solve the network quality problem, the FARIMA model is used to predict the network traffic according to the characteristics of the network traffic sequence. Since the orders of autoregressive order p, fractional difference order d and sliding average order q in the FARIMA model are the key parameters in the prediction process. Therefore, in order to make the prediction results more accurate, a hybrid particle swarm-genetic algorithm is introduced to find the optimal order of autoregressive order p and sliding average order q more accurately to achieve better prediction results. The simulation is carried out by Matlab, and the results show that the particle swarm-genetic hybrid algorithm is good for improving the prediction accuracy of network traffic.