DocumentCode
2578449
Title
Study on network traffic prediction techniques
Author
Feng, Huifang ; Shu, Yantai
Author_Institution
Dept. of Comput. Sci., Tianjin Univ., China
Volume
2
fYear
2005
fDate
23-26 Sept. 2005
Firstpage
1041
Lastpage
1044
Abstract
We briefly describe a number of traffic predictors (such as ARIMA, FARIMA, ANN and wavelet-based predictors) and analyze their computational complexity. We compare their performance with MSE, NMSE and computational complexity by simulating the predictors on four wireless network traffic traces and decide the most suitable network traffic predictor based on acceptable performance and accuracy.
Keywords
autoregressive moving average processes; computational complexity; neural nets; radio networks; telecommunication computing; telecommunication traffic; wavelet transforms; artificial neural network; computational complexity; fractional autoregressive integrated moving average; network traffic prediction techniques; wavelet-based predictors; wireless network traffic; Artificial neural networks; Channel allocation; Communication system traffic control; Computational complexity; Computer science; Measurement; Neural networks; Predictive models; Telecommunication traffic; Traffic control;
fLanguage
English
Publisher
ieee
Conference_Titel
Wireless Communications, Networking and Mobile Computing, 2005. Proceedings. 2005 International Conference on
Print_ISBN
0-7803-9335-X
Type
conf
DOI
10.1109/WCNM.2005.1544219
Filename
1544219
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