Backbone Network Traffic Prediction Based on Modified EEMD and Quantum Neural Network

Huang, Wanwei; Zhang, Jianwei; Liang, Shujun; Sun, Haiyan

doi:10.1007/s11277-018-5292-6

Cited by 16 publications

(9 citation statements)

References 14 publications

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“…Similar reports can be found in [ 28 , 64 , 66 , 73 , 74 , 75 ]. In [ 28 ], the approximate entropy (AE) method was used to evaluate the complexity of the decomposed components, and the echo states NN (ESNN), SVR, and ARMA were selected to predict the traffic flow components with high, medium, and low complexities, respectively.…”

Section: Decomposition-reconstruction-based Hybrid Modelssupporting

confidence: 89%

“…In [ 28 ], the approximate entropy (AE) method was used to evaluate the complexity of the decomposed components, and the echo states NN (ESNN), SVR, and ARMA were selected to predict the traffic flow components with high, medium, and low complexities, respectively. Chen et al [ 73 ] slightly modified the EEMD method and adopted a quantum NN (QNN) for traffic flow forecasting. Chen et al [ 74 ] and Tang et al [ 75 ] used an ANN and fuzzy C-means NN (FCMNN) to predict the decomposed traffic flow series, respectively.…”

Section: Decomposition-reconstruction-based Hybrid Modelsmentioning

confidence: 99%

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Survey of Decomposition-Reconstruction-Based Hybrid Approaches for Short-Term Traffic State Forecasting

Chen

Wang

Hua

et al. 2022

Sensors

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Traffic state prediction provides key information for intelligent transportation systems (ITSs) for proactive traffic management, the importance of which has become the reason for the tremendous number of research papers in this field. Over the last few decades, the decomposition-reconstruction (DR) hybrid models have been favored by numerous researchers to provide a more robust framework for short-term traffic state prediction for ITSs. This study surveyed DR-based works for short-term traffic state forecasting that were reported in the past circa twenty years, particularly focusing on how decomposition and reconstruction strategies could be utilized to enhance the predictability and interpretability of basic predictive models of traffic parameters. The reported DR-based models were classified and their applications in this area were scrutinized. Discussion and potential future directions are also provided to support more sophisticated applications. This work offers modelers suggestions and helps to choose appropriate decomposition and reconstruction strategies in their research and applications.

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Section: Decomposition-reconstruction-based Hybrid Modelssupporting

confidence: 89%

Section: Decomposition-reconstruction-based Hybrid Modelsmentioning

confidence: 99%

Survey of Decomposition-Reconstruction-Based Hybrid Approaches for Short-Term Traffic State Forecasting

Chen

Wang

Hua

et al. 2022

Sensors

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“…The EEMD technique is used to decompose the time series into IMF to remove modal aliasing and redundancy. Then he used QNN to process the decomposed IMF and optimize the parameters of the model so that the convergence speed of the hybrid model is improved [47]. However, the model ignores the impact of too large computation scale of Quantum algorithm mechanics.…”

Section: Md-based Hybrid Modelmentioning

confidence: 99%

A comprehensive review on hybrid network traffic prediction model

Shi

Leau

et al. 2021

IJECE

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Network traffic is a typical nonlinear time series. As such, traditional linear and nonlinear models are inadequate to describe the multi-scale characteristics of traffic, thus compromising the prediction accuracy. Therefore, the research to date has tended to focus on hybrid models rather than the traditional linear and non-linear ones. Generally, a hybrid model adopts two or more methods as combined modelling to analyze and then predict the network traffic. Against this backdrop, this paper will review past research conducted on hybrid network traffic prediction models. The review concludes with a summary of the strengths and limitations of existing hybrid network prediction models which use optimization and decomposition techniques, respectively. These two techniques have been identified as major contributing factors in constructing a more accurate and fast response hybrid network traffic prediction.

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“…The network traffic has times series characteristic information and is decomposed by signal decomposition method to obtain characteristic components, which has different characteristic information, and each component is predicted separately, network traffic after prediction classification reduces the modeling complexity and the prediction error, and this combination prediction method has become an important research direction of network traffic prediction 32 . Common times series decomposition methods include empirical mode decomposition (EMD), 33 ensemble empirical mode decomposition (EEMD), 34 and variational mode decomposition (VMD) 35 . Many experiments have proved the feasibility of combination prediction method.…”

Section: Introductionmentioning

confidence: 99%

A novel network traffic combination prediction model

Tian

Song

2022

Int J Communication

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Summary Network has become an indispensable part of public life. To improve network utilization, network performance, network quality, and enhance network security, precise prediction of network traffic is an indispensable method and basis for solving the above problems. In order to accurately predict the network traffic, a novel combination prediction model for network traffic is proposed. In this model, local mean decomposition (LMD), bidirectional long short‐term memory (BiLSTM), and Bayesian optimization algorithm are combined. First, the LMD method decomposes the network traffic time series to obtain several product function (PF) components and a residual by LMD. Then, each PF component and residual is predicted with BiLSTM model. Meanwhile, the Bayesian optimization algorithm is introduced to optimize the hyperparameters of BiLSTM. Finally, the predicted value of each PF component and residual is linearly superimposed to obtain the final predicted value. Through the study of two groups of actual network traffic datasets and compared with a variety of state‐of‐the‐art prediction models, the proposed model has a preferable prediction results by comparison of the results.

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Backbone Network Traffic Prediction Based on Modified EEMD and Quantum Neural Network

Cited by 16 publications

References 14 publications

Survey of Decomposition-Reconstruction-Based Hybrid Approaches for Short-Term Traffic State Forecasting

Survey of Decomposition-Reconstruction-Based Hybrid Approaches for Short-Term Traffic State Forecasting

A comprehensive review on hybrid network traffic prediction model

A novel network traffic combination prediction model

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