Network traffic prediction based on INGARCH model

Kim, Meejoung

doi:10.1007/s11276-020-02431-y

Cited by 20 publications

(17 citation statements)

References 46 publications

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“…Network traffic prediction methods include analytic model method and data-driven method. The representative methods of the first kind include queuing theory model, cell transmission model, and random geometry model [20][21][22][23].…”

Section: Relatedmentioning

confidence: 99%

[Retracted] Network Traffic Prediction via Deep Graph‐Sequence Spatiotemporal Modeling Based on Mobile Virtual Reality Technology

Zhang

Xin

et al. 2021

Wireless Communications and Mobile Computing

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Accurate and real-time network traffic flow forecast holds an important role for network management. Especially at present, virtual reality (VR), artificial intelligence (AI), vehicle-to-everything (V2X), and other technologies are closely combined through the mobile network, which greatly increases the human-computer interaction activities. At the same time, it requires high-throughput, low delay, and high reliable service guarantee. In order to achieve ondemand real-time high-quality network service, we must accurately grasp the dynamic changes of network traffic. However, due to the increase of client mobility and application behavior diversity, the complexity and dynamics of network traffic in the temporal domain and the spatial domain increase sharply. To accurate capture the spatiotemporal features, we propose the spatial-temporal graph convolution gated recurrent unit (GC-GRU) model, which integrates the graph convolutional network (GCN) and the gated recurrent unit (GRU) together. In this model, the GCN structure could handle the spatial features of traffic flow with network topology, and the GRU is used to further process spatiotemporal features. Experiments show that the GC-GRU model has better prediction performance than other baseline models and can obtain spatial-temporal correlation in traffic lows better.

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Section: Relatedmentioning

confidence: 99%

[Retracted] Network Traffic Prediction via Deep Graph‐Sequence Spatiotemporal Modeling Based on Mobile Virtual Reality Technology

Zhang

Xin

et al. 2021

Wireless Communications and Mobile Computing

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“… tt X For the analysis of time series, the statistical models, such as ARIMA and GARCH, have been considered traditionally, and the NN models, such as multi-layer perceptron (MLP) and LSTM recurrent neural network (RNN), have been used recently (e.g. Kim (2020 and2021) [28,29]). In this study, we consider two statistical models and one DL model.…”

Section: Methodsmentioning

confidence: 99%

Prediction of COVID-19 Confirmed Cases after Vaccination: Based on Statistical and Deep Learning Models

Kim

2021

SciMed. J.

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In this paper, we analyze and predict the number of daily confirmed cases of coronavirus (COVID-19) based on two statistical models and a deep learning (DL) model; the autoregressive integrated moving average (ARIMA), the generalized autoregressive conditional heteroscedasticity (GARCH), and the stacked long short-term memory deep neural network (LSTM DNN). We find the orders of the statistical models by the autocorrelation function and the partial autocorrelation function, and the hyperparameters of the DL model, such as the numbers of LSTM cells and blocks of a cell, by the exhaustive search. Ten datasets are used in the experiment; nine countries and the world datasets, from Dec. 31, 2019, to Feb. 22, 2021, provided by the WHO. We investigate the effects of data size and vaccination on performance. Numerical results show that performance depends on the used data's dates and vaccination. It also shows that the prediction by the LSTM DNN is better than those of the two statistical models. Based on the experimental results, the percentage improvements of LSTM DNN are up to 88.54% (86.63%) and 90.15% (87.74%) compared to ARIMA and GARCH, respectively, in mean absolute error (root mean squared error). While the performances of ARIMA and GARCH are varying according to the datasets. The obtained results may provide a criterion for the performance ranges and prediction accuracy of the COVID-19 daily confirmed cases.Doi: 10.28991/SciMedJ-2021-0302-7 Full Text: PDF

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“…Zhang et al [33] considered a spatiotemporal network with a modified sequence model. Kim came up with an INGARCH model, an enhancement over the previous GARCH models [34]. Researchers have also used some statistical and evolutionary algorithms.…”

Section: Background Workmentioning

confidence: 99%

Prediction of Network Traffic in Wireless Mesh Networks Using Hybrid Deep Learning Model

Mahajan

Kotecha

2022

IEEE Access

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Network traffic prediction based on INGARCH model

Cited by 20 publications

References 46 publications

[Retracted] Network Traffic Prediction via Deep Graph‐Sequence Spatiotemporal Modeling Based on Mobile Virtual Reality Technology

[Retracted] Network Traffic Prediction via Deep Graph‐Sequence Spatiotemporal Modeling Based on Mobile Virtual Reality Technology

Prediction of COVID-19 Confirmed Cases after Vaccination: Based on Statistical and Deep Learning Models

Prediction of Network Traffic in Wireless Mesh Networks Using Hybrid Deep Learning Model

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