NDLI: Time series matrix factorization prediction of internet traffic matrices

Content Provider	IEEE Xplore Digital Library
Author	Yunlong Song Min Liu Shaojie Tang Xufei Mao
Copyright Year	2012
Description	Author affiliation: Illinois Institute of Technology, Chicago, 60616-3793, USA (Shaojie Tang) \|\| TNLIST, School of Software, Tsinghua University, Beijing 100190, China (Xufei Mao) \|\| Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China (Min Liu) \|\| Institute of Computing Technology, Chinese Academy of Sciences, Graduate University of Chinese Academy of Sciences, Beijing 100190, China (Yunlong Song)
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.
Starting Page	284
Ending Page	287
File Size	301340
Page Count	4
File Format	PDF
ISBN	9781467315654
ISSN	07421303
e-ISBN	9781467315647
DOI	10.1109/LCN.2012.6423629
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-10-22
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Prediction algorithms Forecasting Interpolation Time series analysis Sensitivity Internet Computers matrix interpolation traffic matrices prediction time series forecasting
Content Type	Text
Resource Type	Article

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