Aiming at the long-range dependence and short-range dependence characteristics of
backbone network traffic, a traffic forecasting model based on Modified Ensemble Empirical …

In order to improve the prediction accuracy of network traffic, a novel prediction model based
on ensemble empirical mode decomposition and multiple models is proposed. In this study …

Abstract A novel Quantum Adaptive Particle Swarm Optimization (QAPSO) method is
proposed. In this algorithm, the position encoding of the particle is achieved with quantum …

Network has become an indispensable part of public life. To improve network utilization,
network performance, network quality, and enhance network security, precise prediction of …

The rapid update of computing power leads to exponential data traffic growth, and the
incidence of network attacks is also increasing. It is significantly important to analyze and …

Due to the fact that the fluctuation of network traffic is affected by various factors, accurate
prediction of network traffic is regarded as a challenging task of the time series prediction …

As an important part of data management, network traffic evaluation and prediction can not
only find network anomalies but also judge the future trends of the network. To predict …

In this paper, we introduce the integer-valued generalized autoregressive conditional
heteroscedasticity (INGARCH) as a network traffic prediction model. As the INGARCH is …

Network traffic prediction models can be grouped into two types, single models and
combined ones. Combined models integrate several single models and thus can improve …

Network traffic prediction plays a vital role in effective network management, load evaluation
and security warning. Extreme learning machine has the advantages of fast convergence …