Subscribe

Clearwater Beach, FL, USA USA

Oct. 22, 2012 to Oct. 25, 2012

ISBN: 978-1-4673-1565-4

pp: 284-287

Min Liu , Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China

Shaojie Tang , Illinois Institute of Technology, Chicago, 60616-3793, USA

Xufei Mao , TNLIST, School of Software, Tsinghua University, Beijing 100190, China

ABSTRACT

Traffic matrices (TMs) are very important for traffic engineering and if they can be predicted, the network operations can be made beforehand. However, existing prediction methods are neither accurate nor efficient in practice. In this paper, we utilize the spatio-temporal property and low rank nature to directly predict the total TMs. The problem is that conventional matrix interpolation only works well when elements are missing uniformly and randomly. But in the case of TMs prediction, an entire part of the matrix is unknown. To solve this problem, we utilize some essential properties of TMs and add the time series forecasting into the matrix interpolation. We analyze our algorithm and evaluate its performance. The experiment result shows that our method can predict TMs under an NMAE of 30% in most cases, even predicting all the elements of next 3 weeks.

INDEX TERMS

Prediction algorithms, Forecasting, Interpolation, Time series analysis, Sensitivity, Internet, Computers, matrix interpolation, traffic matrices prediction, time series forecasting

CITATION

Min Liu,
Shaojie Tang,
Xufei Mao,
"Time series matrix factorization prediction of internet traffic matrices",

*LCN*, 2012, 38th Annual IEEE Conference on Local Computer Networks, 38th Annual IEEE Conference on Local Computer Networks 2012, pp. 284-287, doi:10.1109/LCN.2012.6423629