Network traffic prediction based on improved support vector machine

Wang, Qiming; Fan, Aiwan; Shi, Hongyin

doi:10.1007/s13198-016-0412-8

Cited by 18 publications

(9 citation statements)

References 6 publications

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“…However, the kernel functions and the parameters in SVM can only be chosen by experience. Wang et al 23 used a fuzzy analytic hierarchy process to improve SVM and proposed a network traffic prediction method based on the improved SVM. This method has high prediction accuracy with small fluctuations of prediction error, but suffers from high computational complexity and large memory cost.…”

Section: Related Workmentioning

confidence: 99%

Network traffic prediction method based on echo state network with adaptive reservoir

et al. 2020

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Network traffic prediction is of great significance to resource management in cyber‐physical systems (CPSs). In particular, network traffic is a nonlinear time series. Echo state network (ESN) is a new neural network with strong nonlinear processing capacity and short‐term memory capacity, and thus can achieve good performance in predicting nonlinear time series. However, network traffic has various characteristics such as self‐similarity, chaos, mutability. As the core of ESN, the reservoir will be fixed rather than adjustable once it is generated, which limits the prediction performance of ESN in different network traffic. To achieve universal excellent prediction performance, this paper proposes a new network traffic prediction method based on ESN with adaptive reservoir (ESN‐AR). First, the framework of ESN‐AR is constructed for network traffic prediction, in which the idea of generative adversarial network (GAN) is incorporated into ESN to adaptively adjust the reservoir. Specifically, ESN is used as the generative model to predict network traffic and feedforward neural network (FNN) is used as the discriminative model to distinguish between the real network traffic and the predicted network traffic. Second, the adversarial training algorithm of ESN‐AR is proposed to obtain the appropriate reservoir depending on the network traffic characteristics. Finally, ESN‐AR is applied to the prediction of three actual network traffic with different characteristics. Simulation results show that compared with the state‐of‐the‐art models, the proposed method achieves more accurate and stable prediction performance.

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Section: Related Workmentioning

confidence: 99%

Network traffic prediction method based on echo state network with adaptive reservoir

et al. 2020

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“…Third, with the development of machine learning, many nonlinear intelligent neural network models have been proposed. These models can better characterize the nature of network traffic and greatly improve the forecasting performance, such as backpropagation (BP) neural networks [12], support vector machines (SVMs) [13] and other improved models. In [14], since the BP neural network models are prone to fall into local optimal values and overfitting, a genetic algorithm (GA) is used to optimize the algorithm of the BP neural network, which achieves certain improvements regarding the disadvantages of the BP neural network and improves the ability of approximation of the BP neural network.…”

Section: Related Workmentioning

confidence: 99%

A Combined Forecasting Model for Satellite Network Self-Similar Traffic

et al. 2019

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Since satellite network traffic is self-similar and long-range-dependent (LRD), after analyzing current network traffic forecasting models, a satellite network traffic combined forecasting model that is based on the decomposition fruit fly optimization algorithm -extreme learning machine (FOA-ELM) is proposed. This forecasting model decomposes LRD network traffic into multiple short-range dependent (SRD) components via empirical mode decomposition (EMD), applies the FOA-ELM forecasting model to the decomposed high-frequency components, and applies the ELM forecasting model to lowfrequency components. The simulation results show that the forecasting model can improve the forecasting accuracy and forecasting speed, reduce the complexity, and achieve effective and efficient forecasting of satellite network traffic. INDEX TERMS Self-similarity, forecasting model, empirical mode decomposition, fruit fly optimization, extreme learning machine.

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“…11 With the development of network measurement technology, researchers have found that network traffic has a long-term correlation, namely, self-similarity, which cannot be dealt with by these traditional linear prediction model models. The nonlinear prediction model include support vector machine (SVM), 12 least square support vector machine (LSSVM), 13,14 artificial neural networks, [15][16][17][18] and gray model. 19 In recent years, some network traffic models based on deep learning model are proposed and achieve some progress.…”

Section: Introductionmentioning

confidence: 99%

Network traffic prediction method based on autoregressive integrated moving average and adaptive Volterra filter

Tian

2021

Int J Communication

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In the process of controlling network congestion and network management, how to accurately predict network traffic is very important. A network traffic prediction method based on autoregressive integrated moving average (ARIMA) model and adaptive Volterra filter model is proposed in this study. Firstly, ARIMA model is utilized to capture and predict the linear features of network traffic time series. The residual sequence is generated by network traffic sequence minus ARIMA predictive value sequence. The residual sequence only contains the nonlinear characteristics of original network traffic. Then an adaptive Volterra filter is used to train and predict the nonlinear residual sequence of original network traffic. The optimal parameters of adaptive Volterra filter are obtained by improved particle swarm optimization algorithm. Add the linear value predicted by ARIMA and the nonlinear value predicted by adaptive Volterra filter to get the final predicted value. The simulation results show that the proposed prediction method has high accuracy, small prediction error, and high reliability.

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Network traffic prediction based on improved support vector machine

Cited by 18 publications

References 6 publications

Network traffic prediction method based on echo state network with adaptive reservoir

Network traffic prediction method based on echo state network with adaptive reservoir

A Combined Forecasting Model for Satellite Network Self-Similar Traffic

Network traffic prediction method based on autoregressive integrated moving average and adaptive Volterra filter

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