time series matrix factorization prediction of internet traffic matrices
文献类型：会议论文
| 作者 | Song Yunlong ; Liu Min ; Tang Shaojie ; Mao Xufei |
| 出版日期 | 2012 |
| 会议名称 | 37th Annual IEEE Conference on Local Computer Networks, LCN 2012 |
| 会议日期 | October 22, 2012 - October 25, 2012 |
| 会议地点 | Clearwater, FL, United states |
| 关键词 | Interpolation Telecommunication traffic Time series |
| 页码 | 284-287 |
| 中文摘要 | 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. |
| 英文摘要 | 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. |
| 收录类别 | EI |
| 会议主办者 | IEEE Computer Society; IEEE Comput. Soc. Tech. Comm. Comput. Commun. (TCCC) |
| 会议录 | Proceedings - Conference on Local Computer Networks, LCN
 |
| 语种 | 英语 |
| ISBN号 | 9781467315647 |
| 源URL | [http://ir.iscas.ac.cn/handle/311060/15909]  |
| 专题 | 软件研究所_软件所图书馆_会议论文 |
| 推荐引用方式 GB/T 7714 | 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. |
入库方式： OAI收割
来源：软件研究所
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