Multi-step network traffic prediction using echo state network with a selective error compensation strategy

Abstract

Communication networks grow exponentially in this globalization era; thus, the network traffic modelling and prediction plays a crucial role in network management and security warning. Solely, the multi-step network traffic prediction may involve greater errors hence worsening prediction performance. To overcome this problem, an optimized echo state network model with selective error compensation is proposed. In the optimized echo state network-based multi-step prediction model, an improved fruit–fly optimization algorithm based on cloud model (named LVCMFOA) is used to select optimum values of four key parameters of the model. The proposed LVCMFOA algorithm uses the levy-flight function to redefine the generation of the fruit–fly population, which can randomly change the search radius and help getting out of a possible local optimal solution and prevent local optimum. To reduce the calculation time but improve the prediction accuracy simultaneously, a sophisticated selective error compensation strategy employing the variable sliding window technology is proposed so as to avoid the error accumulation problem in the multi-step prediction. The effectiveness of the proposed method is verified by applying it to Henon mapping chaotic series, Mackey–Glass chaotic series and two public network traffic data sets all known in the literature.

Grey Wolf Optimization–Based Deep Echo State Network for Time Series Prediction

“…To enhance the operational efficiency, Echo State Networks (ESNs) were proposed by Jaeger and Haas (2004) as a novel RNN variant that can be efficiently utilized for time series forecasting. For example, Han et al (2021) proposed an optimized ESN model with adaptive error compensation for network traffic prediction. Liu et al (2020) proposed a hybrid time-series prediction approach with the binary grey wolf algorithm and echo state networks (BGWO-ESN).…”

Network Traffic Identification Based on Improved EM Algorithm

“…To improve prediction accuracy, variable sliding window technology is also introduced. The experimental results show that this method performs well on multiple datasets [11].…”

“…Since the Z matrix is an unknown value, it can be optimized using the EM algorithm to achieve optimal classification performance. Therefore, the Z matrix is defined as an M K × matrix, as shown in equation (11).…”

A novel network traffic combination prediction model

“…Linear method based on traditional time series includes autoregressive moving average (ARMA), 7,8 autoregressive integrated moving average (ARIMA), 9,10 fractional autoregressive integrated moving average (FARIMA), 11,12 and Holt-Winters exponential smoothing. 13 Nonlinear methods based on machine learning comprise of support vector machine (SVM), 14 least squares support vector machine (LSSVM), 15,16 Kalman filtering model, 17 gray model, 18 artificial neural network (ANN) such as multilayer perceptron (MLP), 19 RBF neural network, 20 echo state network (ESN), 21,22 and BP neural network. 23 With development of the network technology, Poisson or Markov distributions cannot accurately describe the characteristics of network traffic.…”