A Short-Term Forecasting Algorithm for Network Traffic Based on Chaos Theory and SVM

Authors:
Xingwei Liu;Xuming Fang;Zhenhua Qin;Chun Ye;Miao Xie
Affiliations:
School of Mathematics and Computer Engineering, Xihua University, Chengdu, China 610039 and Key Laboratory of Information Coding and Transmission, Southwest Jiaotong University, Chengdu, China 610 ...;Key Laboratory of Information Coding and Transmission, Southwest Jiaotong University, Chengdu, China 610031;School of Mathematics and Computer Engineering, Xihua University, Chengdu, China 610039;School of Mathematics and Computer Engineering, Xihua University, Chengdu, China 610039;School of Mathematics and Computer Engineering, Xihua University, Chengdu, China 610039
Venue:
Journal of Network and Systems Management
Year:
2011

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Abstract

Recently, the forecasting technologies for network traffic have played a significant role in network management, congestion control and network security. Forecasting algorithms have also been investigated for decades along with the development of Time Series Analysis (TSA). Chaotic Time Series Analysis (CTSA) may be used to model and forecast the time series by Chaos Theory. As one of the prevailing intelligent forecasting algorithms, it is worthwhile to integrate CTSA and Support Vector Machine (SVM). In this paper, after the vulnerabilities of Local Support Vector Machine (LSVM) in forecasting modeling are analyzed, the Dynamic Time Wrapping (DTW) and the "Dynamic K" strategy are introduced, as well as a short-term network traffic forecasting algorithm LSVM-DTW-K based on Chaos Theory and SVM is presented. Finally, two sets of network traffic datasets collected from wired and wireless campus networks, respectively, are studied for our experiments.