Network traffic prediction model based on linear and nonlinear model combination

Abstract

We propose a network traffic prediction model based on linear and nonlinear model combination. Network traffic is modeled by an autoregressive moving average model, and the error between the measured and predicted network traffic values is obtained. Then, an echo state network is used to fit the prediction error with nonlinear components. In addition, an improved slime mold algorithm is proposed for reservoir parameter optimization of the echo state network, further improving the regression performance. The predictions of the linear (autoregressive moving average) and nonlinear (echo state network) models are added to obtain the final prediction. Compared with other prediction models, test results on two network traffic datasets from mobile and fixed networks show that the proposed prediction model has a smaller error and difference measures. In addition, the coefficient of determination and index of agreement is close to 1, indicating a better data fitting performance. Although the proposed prediction model has a slight increase in time complexity for training and prediction compared with some models, it shows practical applicability.

Short-Term Exhaust Gas Temperature Trend Prediction of a Marine Diesel Engine Based on an Improved Slime Mold Algorithm-Optimized Bidirectional Long Short-Term Memory—Temporal Pattern Attention Ensemble Model

“…To objectively evaluate the accuracy of the prediction results, it is necessary to establish corresponding evaluation indicators to verify the effectiveness and feasibility of the proposed experimental method. This paper aims to adopt the mean square error (MSE), the mean absolute percentage error (MAPE), the coefficient of determination (R 2 ), and RMSE as the evaluation index for assessing the accuracy of predictions [37,38]. The specific calculation equations are as follows: 22)…”