Network Traffic Prediction Method Based on Extreme Learning Machine with ARIMA Compensation

For the problem of network traffic prediction, we propose a network traffic prediction method based on an autoregressive integrated moving average model (ARIMA) compensation extreme learning machine (ELM), combining the network traffic sequence self-similar analysis. Firstly, one-step network traffic is predicted by the extreme learning machine, and then the sequence of network traffic forecast error is corrected by the ARIMA model. Finally, we obtain the network traffic by adding the predictive values of ELM and ARIMA. We compare the proposed ARIMA method with least squares support vector machine and Elman neural network prediction model. Simulation results show that the proposed method has higher prediction accuracy than the either.