A network traffic prediction method using two-dimensional correlation and Single exponential smoothing

Wireless traffic prediction plays an important role in network planning and management, especially for real-time decision making and short-term prediction. Systems require high accuracy, low cost, and low computational complexity prediction methods. Although exponential smoothing is an effective method, there is a lack of use with cellular networks and research on data traffic. The accuracy and suitability of this method need to be evaluated using several types of traffic. Thus, this study introduces the application of exponential smoothing as a method of adaptive forecasting of cellular network traffic for cases of voice (in Erlang) and data (in megabytes or gigabytes). Simple and Error, Trend, Seasonal (ETS) methods are used for exponential smoothing. By investigating the effect of their smoothing factors in describing cellular network traffic, the accuracy of forecast using each method is evaluated. This research comprises a comprehensive analysis approach using multiple case study comparisons to determine the best fit model. Different exponential smoothing models are evaluated for various traffic types in different time scales. The experiments are implemented on real data from a commercial cellular network, which is divided into a training data part for modeling and test data part for forecasting comparison. This study found that ETS framework is not suitable for hourly voice traffic, but it provides nearly the same results with Holt–Winter’s multiplicative seasonal (HWMS) in both cases of daily voice and data traffic. HWMS is presumably encompassed by ETC framework and shows good results in all cases of traffic. Therefore, HWMS is recommended for cellular network traffic prediction due to its simplicity and high accuracy.

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A New Hybrid Network Traffic Prediction Method

2010 IEEE Global Telecommunications Conference GLOBECOM 2010 ◽

10.1109/glocom.2010.5684249 ◽

2010 ◽

Cited By ~ 13

Author(s):

Lin Xiang ◽

Xiao-Hu Ge ◽

Chuang Liu ◽

Lei Shu ◽

Cheng-Xiang Wang

Keyword(s):

Network Traffic ◽

Prediction Method ◽

Hybrid Network ◽

Traffic Prediction

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Prediction Based on PSO-SVM in Power Communication Network Traffic

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.602-605.2889 ◽

2014 ◽

Vol 602-605 ◽

pp. 2889-2892

Author(s):

Zhen Dong Zhao ◽

Rui Ju Xiao ◽

Meng Meng Pei ◽

Yi Zhou

Keyword(s):

Communication Network ◽

Electric Power ◽

Network Traffic ◽

Prediction Method ◽

Parameters Optimization ◽

Traffic Prediction ◽

Model Parameters ◽

Economy Running ◽

Important Basis

Power communication network traffic prediction is important basis of safely assigning and economically running. The forecasting precision will directly affect the reliability, economy running and supplying power quality of power system. Paper first expounds the electric power communication network traffic prediction research present situation, summarized the characteristics of the forecast and the influencing factors, summarizes the commonly used method, is put forward to the return of the electric power communication network traffic based on libsvm prediction method, and the PSO (particle swarm optimization) algorithm is adopted to model parameters optimization, with the test set error as the decision, based on the optimization of model parameters, choice, makes the prediction precision is improved.

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Network Traffic Prediction Based on Deep Belief Network and Spatiotemporal Compressive Sensing in Wireless Mesh Backbone Networks

Wireless Communications and Mobile Computing ◽

10.1155/2018/1260860 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10 ◽

Cited By ~ 12

Author(s):

Laisen Nie ◽

Xiaojie Wang ◽

Liangtian Wan ◽

Shui Yu ◽

Houbing Song ◽

...

Keyword(s):

Compressive Sensing ◽

Network Traffic ◽

Prediction Method ◽

Deep Belief Network ◽

Mesh Network ◽

Traffic Prediction ◽

Discrete Wavelet ◽

Belief Network ◽

Wireless Mesh ◽

Low Pass

Wireless mesh network is prevalent for providing a decentralized access for users and other intelligent devices. Meanwhile, it can be employed as the infrastructure of the last few miles connectivity for various network applications, for example, Internet of Things (IoT) and mobile networks. For a wireless mesh backbone network, it has obtained extensive attention because of its large capacity and low cost. Network traffic prediction is important for network planning and routing configurations that are implemented to improve the quality of service for users. This paper proposes a network traffic prediction method based on a deep learning architecture and the Spatiotemporal Compressive Sensing method. The proposed method first adopts discrete wavelet transform to extract the low-pass component of network traffic that describes the long-range dependence of itself. Then, a prediction model is built by learning a deep architecture based on the deep belief network from the extracted low-pass component. Otherwise, for the remaining high-pass component that expresses the gusty and irregular fluctuations of network traffic, the Spatiotemporal Compressive Sensing method is adopted to predict it. Based on the predictors of two components, we can obtain a predictor of network traffic. From the simulation, the proposed prediction method outperforms three existing methods.

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Power communication network traffic prediction based on two dimensional prediction algorithms

Proceedings of the 2016 4th International Conference on Electrical & Electronics Engineering and Computer Science (ICEEECS 2016) ◽

10.2991/iceeecs-16.2016.187 ◽

2016 ◽

Author(s):

Lei Xiao ◽

Xue Yang ◽

Min Zhu ◽

Lipeng Zhu

Keyword(s):

Communication Network ◽

Network Traffic ◽

Traffic Prediction ◽

Two Dimensional ◽

Prediction Algorithms

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AGG: A Novel Intelligent Network Traffic Prediction Method Based on Joint Attention and GCN-GRU

Security and Communication Networks ◽

10.1155/2021/7751484 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Huaifeng Shi ◽

Chengsheng Pan ◽

Li Yang ◽

Xiangxiang Gu

Keyword(s):

Joint Attention ◽

Network Traffic ◽

Prediction Method ◽

Temporal Correlation ◽

Absolute Error ◽

Spatial Dimension ◽

Traffic Prediction ◽

Intelligent Network ◽

Time Points ◽

Temporal Features

Timely and accurate network traffic prediction is a necessary means to realize network intelligent management and control. However, this work is still challenging considering the complex temporal and spatial dependence between network traffic. In terms of spatial dimension, links connect different nodes, and the network traffic flowing through different nodes has a specific correlation. In terms of spatial dimension, not only the network traffic at adjacent time points is correlated, but also the importance of distant time points is not necessarily less than the nearest time point. In this paper, we propose a novel intelligent network traffic prediction method based on joint attention and GCN-GRU (AGG). The AGG model uses GCN to capture the spatial features of traffic, GRU to capture the temporal features of traffic, and attention mechanism to capture the importance of different temporal features, so as to realize the comprehensive consideration of the spatial-temporal correlation of network traffic. The experimental results on an actual dataset show that, compared with other baseline models, the AGG model has the best performance in experimental indicators, such as root mean square error (RMSE), mean absolute error (MAE), accuracy (ACC), determination coefficient ( R 2 ), and explained variance score (EVS), and has the ability of long-term prediction.

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