Network Traffic Prediction and Applications Based on Time Series Model

2007 ◽  
pp. 1306-1315
Author(s):  
Jun Lv ◽  
Xing Li ◽  
Tong Li
Keyword(s):  
Time Series ◽  
Network Traffic ◽  
Time Series Model ◽  
Traffic Prediction

Network traffic prediction based on a new time series model

2005 ◽  
Vol 18 (8) ◽  
pp. 711-729 ◽  
Author(s):  
Hao Yin ◽  
Chuang Lin ◽  
Berton Sebastien ◽  
Bo Li ◽  
Geyong Min
Keyword(s):  
Time Series ◽  
Network Traffic ◽  
Time Series Model ◽  
Traffic Prediction ◽  
New Time

scholarly journals Prediction of Traffic Generated by IoT Devices Using Statistical Learning Time Series Algorithms

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Shilpa P. Khedkar ◽  
R. Aroul Canessane ◽  
Moslem Lari Najafi
Keyword(s):  
Time Series ◽  
Network Traffic ◽  
Prediction Models ◽  
Forecast Model ◽  
Congestion Management ◽  
Traffic Prediction ◽  
Traffic Forecasting ◽  
Highly Sensitive ◽  
Iot Devices ◽  
Prediction Techniques

An IoT is the communication of sensing devices linked to the Internet in order to communicate data. IoT devices have extremely critical reliability with an efficient and robust network condition. Based on enormous growth in devices and their connectivity, IoT contributes to the bulk of Internet traffic. Prediction of network traffic is very important function of any network. Traffic prediction is important to ensure good system efficiency and ensure service quality of IoT applications, as it relies primarily on congestion management, admission control, allocation of bandwidth to the system, and the identification of anomalies. In this paper, a complete overview of IoT traffic forecasting model using classic time series and artificial neural network is presented. For prediction of IoT traffic, real network traces are used. Prediction models are evaluated using MAE, RMSE, and R -squared values. The experimental results indicate that LSTM- and FNN-based predictive models are highly sensitive and can therefore be used to provide better performance as a timing sequence forecast model than the conventional traffic prediction techniques.

Comparative study of time series model in traffic prediction of GPRS cells

2010 ◽  
Vol 30 (4) ◽  
pp. 884-887 ◽  
Author(s):  
Xin ZHOU ◽  
Jin ZHANG ◽  
Yan-ke ZHAO ◽  
Ru-long WANG
Keyword(s):  
Time Series ◽  
Comparative Study ◽  
Time Series Model ◽  
Traffic Prediction

scholarly journals Multi-objective Optimization Algorithm for Multimedia English Teaching (MOAMET) Based on Computer Network Traffic Prediction Model

2018 ◽  
Vol 13 (03) ◽  
pp. 58 ◽  
Author(s):  
Wei Wei Feng
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Optimization Algorithm ◽  
Network Traffic ◽  
Computer Network ◽  
Reference Points ◽  
Traffic Prediction ◽  
Multi Objective Optimization ◽  
English Teaching ◽  
Multi Objective

In order to solve the problem of multi-objective optimization for multimedia English teaching, this paper proposes a multi-objective optimization algorithm for multimedia English teaching (MOAMET) based on computer network traffic prediction model, which is based on the computer network traffic prediction model strategy. This algorithm establishes time series for individuals correlated to same reference points, and for such time series through computer network traffic model optimizes multimedia English teaching objectives. Meanwhile, it feeds back the prediction error of the historical moment to the current prediction to improve the accuracy of the optimization, and adds disturbance in each optimized individual to increase the diversity of initial multimedia English teaching so as to speed up the convergence speed of the algorithm in the new environment. Through experiments it teats the algorithm, also makes comparison and analysis with two existing algorithms, the results show that the proposed algorithm can maintain good performance in dealing with multi-objective optimization for multimedia English teaching.

An improved fruit fly algorithm-unscented Kalman filter-echo state network method for time series prediction of the network traffic data with noises

2020 ◽  
Vol 42 (7) ◽  
pp. 1281-1293
Author(s):  
Ying Han ◽  
Yuanwei Jing ◽  
Georgi M Dimirovski
Keyword(s):  
Time Series ◽  
Kalman Filter ◽  
Network Traffic ◽  
Unscented Kalman Filter ◽  
Prediction Method ◽  
Fruit Fly ◽  
Traffic Prediction ◽  
Echo State Network ◽  
Time Varying ◽  
Traffic Data

With the complexity of the network system rapidly increasing, network traffic prediction has great significance for the safety pre-warning of the network load, network management and control, and improvement of the quality of the network service. In this paper, the time series analysis is used for the network traffic prediction, and a prediction method combined with an optimized unscented Kalman filter (UKF) by an improved fruit fly algorithm (IFOA) and echo state network (ESN) is proposed, which is named by IFOA-UKF-ESN. The researches mainly solve the problem that the prediction accuracy might be greatly affected by the actual network traffic data with unknown and time-varying noises. UKF is used to train the best state vector (formed by spectral radius, scale of the reservoir, scale of the input units and connectivity rate) of ESN; and the proposed IFOA algorithm is proposed to optimize the weights of the predicted state value and the covariance in UKF, which makes UKF have adaptive ability for unknown and time-varying noise. Three actual network traffic data sets with different Gaussian white noise distributions are constructed for experiments, and the experimental results show that the proposed prediction method makes an average improvement by reducing at least 20.60%, 43.23% and 41.85% of RMSE, at least 23.66%, 52.38% and 47.50% of MAE, and at least 23.58%, 52.10% and 47.28% of MAPE, which verify the effectiveness of the proposed method.

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