| Title: | time series matrix factorization prediction of internet traffic matrices |
| Author: | Song Yunlong
; Liu Min
; Tang Shaojie
; Mao Xufei
|
| Source: | Proceedings - Conference on Local Computer Networks, LCN
|
| Conference Name: | 37th Annual IEEE Conference on Local Computer Networks, LCN 2012
|
| Conference Date: | October 22, 2012 - October 25, 2012
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| Issued Date: | 2012
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| Conference Place: | Clearwater, FL, United states
|
| Keyword: | Interpolation
; Telecommunication traffic
; Time series
|
| Indexed Type: | EI
|
| ISBN: | 9781467315647
|
| Department: | (1) Institute of Computing Technology Chinese Academy of Sciences Graduate University of Chinese Academy of Sciences Beijing 100190 China; (2) Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China; (3) Illinois Institute of Technology Chicago IL 60616-3793 United States; (4) TNLIST School of Software Tsinghua University Beijing 100190 China
|
| Sponsorship: | IEEE Computer Society; IEEE Comput. Soc. Tech. Comm. Comput. Commun. (TCCC)
|
| 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. © 2012 IEEE. |
| English 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. © 2012 IEEE. |
| Language: | 英语
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| Content Type: | 会议论文
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| URI: | http://ir.iscas.ac.cn/handle/311060/15909
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| Appears in Collections: | 软件所图书馆_会议论文
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| Recommended Citation: | |
Song Yunlong,Liu Min,Tang Shaojie,et al. time series matrix factorization prediction of internet traffic matrices[C]. 见:37th Annual IEEE Conference on Local Computer Networks, LCN 2012. Clearwater, FL, United states. October 22, 2012 - October 25, 2012.
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