4K video traffic analysis using seasonal autoregressive model for traffic prediction

Markovic, Dejan R.; Gavrovska, Ana; Reljin, Irini

doi:10.1109/telfor.2016.7818885

Cited by 7 publications

(5 citation statements)

References 6 publications

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“…Updating W: Since D (k) is the diagonal matrix composed of the kth row in U (3) , we calculate W through D (k) . Formula ( 13) can be simplified as:…”

Section: An Algorithm For Solving Dmd-lrtmentioning

confidence: 99%

“…So far, various methods have been proposed for time series prediction. Since spatial and temporal correlations are the main characters of traffic data, plenty of research work considers the time series as variables, aiming at exploring the mapping relations between historical time series and the time spots to be predicted, for instance, the autoregressive models [2, 3]. Nevertheless, statistical approaches are limited by the number of parameters and are difficult to handle traffic data with complex structure and multiple sources.…”

Section: Introductionmentioning

confidence: 99%

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Traffic forecasting with missing data via low rank dynamic mode decomposition of tensor

Zhang

Wei

et al. 2022

IET Intelligent Trans Sys

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Traffic forecasting is an important part in realising intelligent traffic management, which helps traffic controllers and travellers make effective decisions. However, traffic forecasting accuracy is often affected by missing traffic data due to hardware and software failure. Therefore, accurate prediction based on incomplete traffic data is an important problem as well as a challenge. Though many approaches recover the missing values before prediction, the errors from the data‐filling step are likely to cause additional bias to the prediction result. Besides, this tactic is difficult to guarantee the timeliness and may impede real‐time prediction. In this case, a traffic forecasting model is proposed to directly predict traffic data with missing values. This model develops tensor formed dynamic mode decomposition, recording the dynamic information of traffic data into a state transition tensor. In addition, the model takes low rank property of the dynamic tensor and the similarity of temporal variation trend into consideration. In order to verify the effectiveness and the robustness of the proposed model, experiments were performed on two real‐world time series datasets. The results demonstrate that the model achieves better performance on forecasting than other baseline approaches under the impact of missing data.

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“…Updating W: Since D (k) is the diagonal matrix composed of the kth row in U (3) , we calculate W through D (k) . Formula ( 13) can be simplified as:…”

Section: An Algorithm For Solving Dmd-lrtmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Traffic forecasting with missing data via low rank dynamic mode decomposition of tensor

Zhang

Wei

et al. 2022

IET Intelligent Trans Sys

View full text Add to dashboard Cite

show abstract

“…It could achieve a nonlinear approximation up to 300-step ahead prediction. In [28], a seasonal autoregressive method was used for high definition video stream modeling. In [29], a Markovian model was presented for the specific Bframe prediction, aiming at reducing bandwidth requirements and smoothing the video stream.…”

Section: Introductionmentioning

confidence: 99%

Smoothing-Aided Support Vector Machine Based Nonstationary Video Traffic Prediction Towards B5G Networks

Wang

Sun

et al. 2020

IEEE Trans. Veh. Technol.

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Video services have hold a surprising proportion of the whole network traffic in wireless communication networks. Accurate prediction of video traffic can endow networks with intelligence in resource management, especially for the forthcoming beyond the fifth-generation (B5G) networks. However, the existing approaches fail to accurately predict video traffic with all types of frames, due to the natures of strong long-range dependence, self-similarity and burstiness. Obviously, it is unable to meet the QoS and QoE requirements of dynamic bandwidth allocation. In this paper, we propose the feasibility of advanced machine learning methodology applied in nonstationary video traffic prediction, i.e., smoothing-aided support vector machine (SSVM) model. The model utilizes classical smoothing methods to preprocess video traffic by relieving the drastic fluctuation of video stream. It can provide an effective association for the subsequent support vector regression, as the preprocessed data becomes more smooth and continuous than the original unprocessed one. Experimental results show that our proposed model significantly outperforms the state of the art model, i.e., logistic smooth transition autoregressive, in prediction performance. The superior nonlinear approximation capacity is further demonstrated by visualized statistical analysis.

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“…Some traffic models for video have been studied in previous researches. Most models are based on autoregressive models and their combinations, which fit the autocorrelation of video traffic well with low complexity [4]. I. Spanou proposed Discrete Autoregrassive modeling which is based on scene change detection [5].…”

Section: Introductionmentioning

confidence: 99%

Traffic Model for Teleconsultation Based on the Service Characteristics

Yang¹,

Zhang²,

Kang³

2018

Proceedings of the 2018 3rd International Conference on Communications, Information Management and Network Security (CIMNS 2018

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The rapid development of numerous emerging network services poses a challenge to the network resources allocation with their bandwidth requirements and statistical characteristics. Network traffic modeling plays an important role in network resources allocation which can be used to guarantee Quality-of-Service (QoS) of these new services. In this study, we analyzed the service composition and traffic characteristics of the spreading teleconsultation service and establish a traffic model. The obtained experimental results show that the proposed methodology which combines discrete autoregressive model (DAR) and wavelet analysis provides good accuracy in teleconsultation service traffic modeling. Keywords-service traffic model; video traffic; discrete autoregressive model; wavelet model I. As Figure 1 shows, teleconsultation service provides video communication, medical images and teleconsultation text transmission between patient and doctor. Teleconsultation holds about ten to fifteen minutes. At the beginning of the teleconsultation process, medical images are transmitted and then there will be teleconsultation text transmission. Video transmission occurs throughout the process, so it is obvious that

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4K video traffic analysis using seasonal autoregressive model for traffic prediction

Cited by 7 publications

References 6 publications

Traffic forecasting with missing data via low rank dynamic mode decomposition of tensor

Traffic forecasting with missing data via low rank dynamic mode decomposition of tensor

Smoothing-Aided Support Vector Machine Based Nonstationary Video Traffic Prediction Towards B5G Networks

Traffic Model for Teleconsultation Based on the Service Characteristics

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