A Novel Traffic Prediction Method Based on IMF

Article Preview

Abstract:

This paper proposes an IMF-based traffic prediction method. According with the characteristics of each IMF (Intrinsic Mode Function), different models are adopted for the prediction of IMFs. Experiments are carried out based on real traffic. And the experimental results show that the proposed method not only solves mode mixing but also increases the prediction accuracy.

You might also be interested in these eBooks

Info:

Periodical:

Advanced Materials Research (Volumes 490-495)

Pages:

1421-1425

Citation:

Online since:

March 2012

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2012 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] George Xylomenos, Konstantinos Katsaros, Vasilis Tsakanikas, Support of multiple content variants in the multimedia broadcast/multicast service, International Journal of Communication Systems. 24 (2011) 691–708.

DOI: 10.1002/dac.1175

Google Scholar

[2] J. Ilow, Forecasting network traffic using FARIMA models with heavy tailed innovations, Proceedings of ICASSP. 6 (2000) 3814-3817.

DOI: 10.1109/icassp.2000.860234

Google Scholar

[3] Gang Tong, Chunling Fan, et al, Fuzzy Neural Network Model Applied in the Traffic Flow Prediction, IEEE International Conference on Information Acquisition. (2006) 1229–1233.

DOI: 10.1109/icia.2006.305923

Google Scholar

[4] WANG H J, SHEN L, LIU H Y, Adjustments based on wavelet transform ARIMA model for network traffic prediction. Proceedings of International Conference on Computer Engineering and Technology. (2010) 520–523.

DOI: 10.1109/iccet.2010.5485432

Google Scholar

[5] Zhihui, Z., Yunlian, S., Yu, J., Short term Load Forecasting Based on EMD and SVM, High Voltage Engineering. 33 (2007) 118-122.

Google Scholar

[6] HUANG N E, SHEN Z, LONG S R, The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis, Proc Royal Soc London A. (1998) 903-995.

DOI: 10.1098/rspa.1998.0193

Google Scholar

[7] R. K. NIAZY, C. F. BECKMANN, et al, Performance evaluation of ensemble empirical mode decomposition, Advances in Adaptive Data Analysis. 1 (2009) 231–242.

DOI: 10.1142/s1793536909000102

Google Scholar

[8] WU Z H, HUANG N E, Ensemble empirical mode decomposition: a noise-assisted data analysis method, Advances in Adaptive Data Analysis. 1 (2009) 1–41.

DOI: 10.1142/s1793536909000047

Google Scholar