Network Traffic Prediction Model Based on Auto-regressive Moving Average

Xu, Shuona; Zeng, Biqing

doi:10.4304/jnw.9.3.653-659

Cited by 16 publications

(7 citation statements)

References 14 publications

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“…The linear prediction model can only be used to predict the short‐term network traffic. Typical linear models include autoregressive (AR) model, 9 AR moving average (ARMA), 10 and ARIMA model 11,12 . The principle of linear traffic prediction model is to fit polynomials that can reflect the trend characteristics of past network traffic data and then use these polynomials to predict the future value.…”

Section: Literature Reviewmentioning

confidence: 99%

Network traffic prediction model based on ensemble empirical mode decomposition and multiple models

Lian

Tian

2021

Int J Communication

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Summary 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, ensemble empirical mode decomposition algorithm is introduced to decompose the network traffic and get several components. Approximate entropy is introduced to judge the complexity of each component. According to the results of approximate entropy, echo state network is selected to predict high complexity components, support vector machine is used to predict medium complexity components, and autoregressive integrated moving average model is introduced to predict low complexity components. The advantages of each prediction model are used to predict the appropriate component. The final prediction results are obtained by adding the predicted values of each component. In order to solve the problem that the prediction performance of support vector machine and echo state network is affected by their parameters, an improved whale optimization algorithm is proposed to optimize the parameters of the model. Meanwhile, the calculation results of approximate entropy show that compared with the original network traffic, the complexity of each component obtained by ensemble empirical mode decomposition is reduced, which reduces the complexity of modeling. Three network traffic datasets with sampling periods of 10 ms, 1 s, and 10 min are collected. Compared with the other five state‐of‐the‐art prediction models, the case study results show that the proposed prediction model has better prediction accuracy and excellent statistical performance indicators.

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Section: Literature Reviewmentioning

confidence: 99%

Network traffic prediction model based on ensemble empirical mode decomposition and multiple models

Lian

Tian

2021

Int J Communication

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“…Fig. 12 shows the comparison between predicted value and actual value of network traffic by ARMA model in literature [18], Fig. 13 shows the comparison between predicted value and actual value of network traffic by LSSVM in literature [9].…”

Section: Simulationmentioning

confidence: 99%

“…At present, many scholars made a lot of research work for telecommunication and Ethernet network traffic prediction. Some linear prediction models such as auto regressive moving average (ARMA) [6,18], auto regressive integrated moving average (ARIMA) [16,19] and fractional auto regressive integrated moving average (FARIMA) [13] were used for network traffic prediction. The literature [5] studied the above linear models, in which the prediction accuracy of each model with different time scales is performed by experiment, through the simulation, the author pointed out that the appropriate time scale of each model.…”

Section: Introductionmentioning

confidence: 99%

A Network Traffic Hybrid Prediction Model Optimized by Improved Harmony Search Algorithm

Tian¹,

Li²,

Wang³

et al. 2015

NNW

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The telecommunication and Ethernet traffic prediction problem is studied. Network traffic prediction is an important problem of telecommunication and Ethernet congestion control and network management. In order to improve network traffic prediction accuracy, a network traffic hybrid prediction model was proposed by using the advantages of grey model and Elman neural network, grey model and Elman neural network predictive values were independently obtained, the different weight coefficients of two prediction models were given. In terms of weight coefficients optimization, an improved harmony search algorithm with better convergence speed and accuracy was proposed, the optimal weight coefficients of network traffic hybrid prediction model were determined through this algorithm, two prediction models results were multiplied by the weight coefficients to obtain the final prediction value. The network traffic sample data from an actual telecommunication network was collected as simulation object. The simulation results verified that the proposed network traffic hybrid prediction model based on improved harmony search algorithm has higher prediction accuracy.

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“…Statistical models such as Gaussian and Poisson distributions have been adopted to depict the traffic very early [ 3 ]. Linear models including autoregressive (AR) [ 4 ], moving average (MA) [ 5 ], autoregressive moving average (ARMA) [ 6 ], and autoregressive integrated moving average (ARIMA) [ 7 ] models, which typically need users to set many empirical parameters. Usually, these linear models are only suitable for short-term traffic prediction, and are insufficient for long-term prediction due to the nonlinear nature, the component complexity, and the self-similar feature [ 8 ] of the traffic flows transmitted in modern networks.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based Traffic Prediction Method for Digital Twin Network

et al. 2023

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Network traffic prediction (NTP) can predict future traffic leveraging historical data, which serves as proactive methods for network resource planning, allocation, and management. Besides, NTP can also be applied for load generation in simulated and emulated as well as digital twin networks (DTNs). This paper focuses on accurately predicting background traffic matrix (TM) of typical local area network (LAN) for traffic synchronization in DTN. A survey is firstly conducted on DTN, conventional model, and deep learning based NTP methods. Then, as the major contribution, a linear feature enhanced convolutional long short-term memory (ConvLSTM) model based NTP method is proposed for LAN. An autoregressive unit is integrated into the ConvLSTM model to improve its linear prediction ability. In addition, this paper further optimizes the proposed model from both spatial and channel-wise dimensions. Particularly, a traffic pattern attention (TPA) block and a squeeze & excitation (SE) block are derived and added to the enhanced ConvLSTM (eConvLSTM) model. Comparative experiments demonstrate that the eConvLSTM model outperforms all the baselines. It can improve the prediction accuracy by reducing the mean square error (MSE) up to 10.6% for one-hop prediction and 16.8% for multi-hops prediction, compared to the legacy CovnLSTM model, with still satisfying the efficiency requirements. The further enhancement of the eConvLSTM model can additionally reduce the MSE about 2.1% for one-hop prediction and 4.2% for multi-hops prediction, with slightly degrading efficiency. The proposed eConvLSTM model based NTP method can play a vital role on DTN traffic synchronization.

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Network Traffic Prediction Model Based on Auto-regressive Moving Average

Cited by 16 publications

References 14 publications

Network traffic prediction model based on ensemble empirical mode decomposition and multiple models

Network traffic prediction model based on ensemble empirical mode decomposition and multiple models

A Network Traffic Hybrid Prediction Model Optimized by Improved Harmony Search Algorithm

Deep Learning Based Traffic Prediction Method for Digital Twin Network

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