Time series matrix factorization prediction of internet traffic matrices

Yunlong Song*, Min Liu, Shaojie Tang, Xufei Mao

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

9 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationProceedings of the 37th Annual IEEE Conference on Local Computer Networks
Subtitle of host publicationLCN 2012
EditorsTom Pfeifer, Anura Jayasumana, Damla Turgut
Place of PublicationPiscataway, NJ
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages4
ISBN (Print)9781467315647
Publication statusPublished - 2012
Externally publishedYes
Event37th Annual IEEE Conference on Local Computer Networks, LCN 2012 - Clearwater, FL, United States
Duration: 22 Oct 201225 Oct 2012


Other37th Annual IEEE Conference on Local Computer Networks, LCN 2012
Country/TerritoryUnited States
CityClearwater, FL


  • traffic matrices prediction
  • time series forecasting
  • matrix interpolation


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