Multiresolution Analysis and Forecasting of Mobile Communication Traffic

PENG Yu; LEI Miao; GUO Jia; PENG Xiyuan

Article Contents

Article Navigation > Chinese Journal of Electronics > 2013 > 22(2): 373-376

PENG Yu, LEI Miao, GUO Jia, et al., “Multiresolution Analysis and Forecasting of Mobile Communication Traffic,” Chinese Journal of Electronics, vol. 22, no. 2, pp. 373-376, 2013,

Citation:

PENG Yu, LEI Miao, GUO Jia, et al., “Multiresolution Analysis and Forecasting of Mobile Communication Traffic,” Chinese Journal of Electronics, vol. 22, no. 2, pp. 373-376, 2013,

PENG Yu, LEI Miao, GUO Jia, et al., “Multiresolution Analysis and Forecasting of Mobile Communication Traffic,” Chinese Journal of Electronics, vol. 22, no. 2, pp. 373-376, 2013,

Citation:

PENG Yu, LEI Miao, GUO Jia, et al., “Multiresolution Analysis and Forecasting of Mobile Communication Traffic,” Chinese Journal of Electronics, vol. 22, no. 2, pp. 373-376, 2013,

PDF( 161 KB)

Multiresolution Analysis and Forecasting of Mobile Communication Traffic

Funds: This work is supported by the Program for New Century Excellent Talents in University (No.NCET-10-0062), the Ph.D. Programs Foundation of Ministry of Education of China (No.20092302110013), and China Mobile Group Heilongjiang Company Limited (No.[2008]1891).

Received Date: 2011-10-01
Rev Recd Date: 2012-08-01
Publish Date: 2013-04-25

Abstract

Abstract

Combined with Fourier spectrum prior knowledge, a novel wavelet multiresolution analysis and forecasting algorithm is proposed. It focuses on long term trend prediction of multi-periodic, non-stationary, mobile communication traffic series. New algorithm calculates the Fourier spectrum for multi-periodic series at first, and takes the prominent period components with definite physical notion as the prior knowledge. After that, it extracts more valuable time domain features with wavelet multiresolution analysis. Finally, it adopts a single model to predict each of them, and integrates the prediction results to gain the final trend prediction of traffic time series. Experimental results on real traffic data show that all isolated components in proposed multiresolution analysis deliver the distinct physical information in traffic data. Additionally, our algorithm can pick out most of prominent period components revealed in the Fourier spectrum and improve prediction accuracy.
- Wavelet multiresolution analysis,
- Traffic forecasting,
- Fourier spectrum

FullText(HTML)

References(13)

References

C.L. Wen, S.W. Wang, et al., "The multiscale parameter estimation methods for a sort of time series", Chinese Journal of Electronics, Vol.18, No.4, pp.660-664, 2009.

W.M. Cao, "The application of direction basis function neural networks to the prediction of chaotic time series", Chinese Journal of Electronics, Vol.13, No.3, pp.390-398, 2004.

H.L. Sun, Y.H. Jin, et al., "Network traffic prediction by a wavelet-based combined model", Chinese Physics B, Vol.18, No.11, pp.4760-4768, 2009.

Y.C. Xie, Y.L. Zhang, Z.R. Ye, "Short-term traffic volume forecasting using Kalman filter with discrete wavelet decomposition", Computer-Aided Civil and Infrastructure Engineering, Vol.22, No.5, pp.326-334, 2007.

X.M. Jiang, H. Adeli, et al., "Dynamic wavelet neural network model for traffic flow forecasting", Journal of Transportation Engineering, ASCE, Vol.131, No.10, pp.771-779, 2005.

A.J. Rocha Reis, A.P. Alves da Silva, "Feature extraction via multiresolution analysis for short-term load forecasting", IEEE Trans. on Power Systems, Vol.20, No.1, pp.189-198, 2005.

Z.C. Li, X. Zhou, Z. Dai, X.Y. Zou, "Classification of G proteins and prediction of GPCRs-G proteins coupling specificity using continuous wavelet transform and information theory", Amino Acids, Vol.43, No.2, pp.793-804, 2012.

P.C. Deka, R. Prahlada, "Discrete wavelet neural network approach in significant wave height forecasting for multistep lead time", Ocean Engineering, Vol.43, No.1, pp.32-42, 2012.

R.P. Liang, S.Y. Huang, et al., "A novel algorithm combining support vector machine with the discrete wavelet transform for the prediction of protein subcellular localization", Computers in Biology and Medicine, Vol.42, No.2, pp.180-187, 2012.

Y.D. Cui, H.L. Sun, "Periodicity impacts on the accuracy in grey model based Internet traffic prediction", Chinese Journal of Electronics, Vol.19, No.1, pp.170-174, 2010.

K. Papagiannaki, et al., "Long-term forecasting of internet backbone traffic", IEEE Trans. on Neural Networks, Vol.16, No.5, pp.1110-1124, 2005.

D.B. Percival, A.T. Walden, Wavelet Methods for Time Series Analysis, Cambridge University Press, Cambridge, U.K., pp.159-205, 2000.

Nobre F.F., Monteiro. A.B.S., et al., "Dynamic linear model and SARIMA: A comparison of their forecasting performance in epidemiology", Statistics in Medicine, Vol.20, No.20, pp.3051- 3069, 2001.

Relative Articles

Supplements(0)

Cited By

Proportional views