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Nonlinear prediction of small scale network traffic based on local relevance vector machine regression model

Meng Qing-Fang Chen Yue-Hui Feng Zhi-Quan Wang Feng-Lin Chen Shan-Shan

Nonlinear prediction of small scale network traffic based on local relevance vector machine regression model

Meng Qing-Fang, Chen Yue-Hui, Feng Zhi-Quan, Wang Feng-Lin, Chen Shan-Shan
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  • Based on the nonlinear time series local prediction method and the relevance vector machine regression model, the local relevance vector machine prediction method is proposed and applied to predict the small scale traffic measurement data, and the BIC-based neighbor point selection method is used to choose the number of nearest-neighbor points for the local relevance vector machine regression model. We also compare the performance of the local relevance vector machine regression model with the feed-forward neural network optimized by particle swarm optimization for the same problem. Experimental results show that the local relevance vector machine prediction method whose neighboring points have been optimized can effectively predict the small scale traffic measurement data, can reproduce the statistical features of real small scale traffic measurements, and the prediction accuracy of the local relevance vector machine regression model is superior to that of the feedforward neural network optimized by PSO and the local linear prediction method.
    • Funds: Project supported by the National Natural Science Foundation of China (Grant Nos. 61201428, 61070130, 61173079), the Natural Science Foundation of Shandong Province, China (Grant Nos. ZR2010FQ020, ZR2011FZ003), the Shandong Distinguished Middle-aged and Young Scientist Encourage and Reward Foundation, China (Grant No. BS2009SW003), and the China Postdoctoral Science Foundation (Grant No. 20100470081).
    [1]

    Orosz G, Krauskopf B, Wilson R E 2005 Physica D 211 277

    [2]

    Orosz G, Wilson R E, Krauskopf B 2004 Phys. Rev. E 70 026207

    [3]

    Gasser I, Sirito G, Werner B 2004 Physica D 197 222

    [4]

    Doulamis A D, Doulamis N D, Kollias S D 2003 IEEE Trans. on Neur. Netw. 14 150

    [5]

    Xie Y B, Wang W X, Wang B H 2007 Phys. Rev. E 75 026111

    [6]

    Zhang Z L, Ribeiro V J, Mooon S, Diot C 2003 IEEE INFOCOM 3 1826

    [7]

    Uglig S 2004 ACM SIGCOMM computer communications review 34

    [8]

    Kantz H, Schreiber T 2003 Nonlinear Time Series Analysis (Second Edition) (Cambridge: Cambridge University Press)

    [9]

    Small M, Tse C K 2002 Phys. Rev. E 66 066701

    [10]

    Karunasinghe D S K, Liong S Y 2006 Journal of Hydrology 323 92

    [11]

    Ma Q L, Zheng Q L, Peng H, Zhong T W, Qin J W 2008 Chin. Phys. B 17 536

    [12]

    Ma Q L, Zheng Q L, Peng H, Qin J W 2009 Acta Phys. Sin. 58 1410 (in Chinese) [马千里, 郑启伦, 彭宏, 覃姜维 2009 物理学报 58 1410]

    [13]

    Liu H, Liu D, Deng L F 2006 Chin. Phys. 15 1196

    [14]

    Chen Q, Ren X M 2010 Acta Phys. Sin. 59 2310 (in Chinese) [陈强, 任雪梅 2010 物理学报 59 2310]

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    Farmer J D, Sidorowich J J 1987 Phys. Rev. Lett. 59 845

    [16]

    Giona M, Lentini F, Cimagalli V 1991 Phys. Rev. A 44 3496

    [17]

    Kugiumtzis D, Lingjarde O C, Christophersen N 1998 Physica D 112 344

    [18]

    Ragwitz M, Kantz H 2002 Phys. Rev. E 65 056201

    [19]

    Meng Q F, Peng Y H 2007 Phys. Lett. A 370 465

    [20]

    Li H C, Zhang J S 2005 Chin. Phys. Lett. 22 2776

    [21]

    Zhang J S, Dang J L, Li H C 2007 Acta Phys. Sin. 56 67 (in Chinese) [张家树, 党建亮, 李恒超 2007 物理学报 56 67]

    [22]

    Du J, Cao Y J, Liu Z J, Xu L J, Jiang Q Y, Guo C X, Lu J G 2009 Acta Phys. Sin. 58 5997 (in Chinese) [杜杰, 曹一家, 刘志坚, 徐立中, 江全元, 郭创新, 陆金桂 2009 物理学报 58 5997]

    [23]

    Meng Q F, Peng Y H, Qu H J, Han M 2008 Acta Phys. Sin. 57 1423 (in Chinese) [孟庆芳, 彭玉华, 曲怀敬, 韩民 2008 物理学报 57 1423]

    [24]

    Shang P, Li X, Kamae S 2005 Chaos Solitons Fractals 25 121

    [25]

    Shang P, Li X, Kamae S 2006 Phys. Lett. A 357 314

    [26]

    Akritas P, Akishin P G, Antoniou I, Bonushkina A Y, Drossinos I 2002 Chaos Solit. and Frac. 14 595

    [27]

    Chen Y H, Yang B, Dong J W, Abraham A 2005 Information Sciences 174 219

    [28]

    Chen Y H, Yang B, Abraham A 2007 Neurocomputing 70 697

    [29]

    Chen Y H, Yang B, Meng Q F 2012 Applied Soft Computing 12 274

    [30]

    Tipping M E 2001 Journal of Machine Learning Research 3 211

    [31]

    Zio E, Maio F D 2012 Expert Systems with Applications 39 10681

    [32]

    Han M, Zhao Y, Yang X L, Lin D 2011 Control Theory and Applications 28 343 (in Chinese) [韩敏, 赵耀, 杨溪林, 林东 2011 控制理论与应用 28 343]

  • [1]

    Orosz G, Krauskopf B, Wilson R E 2005 Physica D 211 277

    [2]

    Orosz G, Wilson R E, Krauskopf B 2004 Phys. Rev. E 70 026207

    [3]

    Gasser I, Sirito G, Werner B 2004 Physica D 197 222

    [4]

    Doulamis A D, Doulamis N D, Kollias S D 2003 IEEE Trans. on Neur. Netw. 14 150

    [5]

    Xie Y B, Wang W X, Wang B H 2007 Phys. Rev. E 75 026111

    [6]

    Zhang Z L, Ribeiro V J, Mooon S, Diot C 2003 IEEE INFOCOM 3 1826

    [7]

    Uglig S 2004 ACM SIGCOMM computer communications review 34

    [8]

    Kantz H, Schreiber T 2003 Nonlinear Time Series Analysis (Second Edition) (Cambridge: Cambridge University Press)

    [9]

    Small M, Tse C K 2002 Phys. Rev. E 66 066701

    [10]

    Karunasinghe D S K, Liong S Y 2006 Journal of Hydrology 323 92

    [11]

    Ma Q L, Zheng Q L, Peng H, Zhong T W, Qin J W 2008 Chin. Phys. B 17 536

    [12]

    Ma Q L, Zheng Q L, Peng H, Qin J W 2009 Acta Phys. Sin. 58 1410 (in Chinese) [马千里, 郑启伦, 彭宏, 覃姜维 2009 物理学报 58 1410]

    [13]

    Liu H, Liu D, Deng L F 2006 Chin. Phys. 15 1196

    [14]

    Chen Q, Ren X M 2010 Acta Phys. Sin. 59 2310 (in Chinese) [陈强, 任雪梅 2010 物理学报 59 2310]

    [15]

    Farmer J D, Sidorowich J J 1987 Phys. Rev. Lett. 59 845

    [16]

    Giona M, Lentini F, Cimagalli V 1991 Phys. Rev. A 44 3496

    [17]

    Kugiumtzis D, Lingjarde O C, Christophersen N 1998 Physica D 112 344

    [18]

    Ragwitz M, Kantz H 2002 Phys. Rev. E 65 056201

    [19]

    Meng Q F, Peng Y H 2007 Phys. Lett. A 370 465

    [20]

    Li H C, Zhang J S 2005 Chin. Phys. Lett. 22 2776

    [21]

    Zhang J S, Dang J L, Li H C 2007 Acta Phys. Sin. 56 67 (in Chinese) [张家树, 党建亮, 李恒超 2007 物理学报 56 67]

    [22]

    Du J, Cao Y J, Liu Z J, Xu L J, Jiang Q Y, Guo C X, Lu J G 2009 Acta Phys. Sin. 58 5997 (in Chinese) [杜杰, 曹一家, 刘志坚, 徐立中, 江全元, 郭创新, 陆金桂 2009 物理学报 58 5997]

    [23]

    Meng Q F, Peng Y H, Qu H J, Han M 2008 Acta Phys. Sin. 57 1423 (in Chinese) [孟庆芳, 彭玉华, 曲怀敬, 韩民 2008 物理学报 57 1423]

    [24]

    Shang P, Li X, Kamae S 2005 Chaos Solitons Fractals 25 121

    [25]

    Shang P, Li X, Kamae S 2006 Phys. Lett. A 357 314

    [26]

    Akritas P, Akishin P G, Antoniou I, Bonushkina A Y, Drossinos I 2002 Chaos Solit. and Frac. 14 595

    [27]

    Chen Y H, Yang B, Dong J W, Abraham A 2005 Information Sciences 174 219

    [28]

    Chen Y H, Yang B, Abraham A 2007 Neurocomputing 70 697

    [29]

    Chen Y H, Yang B, Meng Q F 2012 Applied Soft Computing 12 274

    [30]

    Tipping M E 2001 Journal of Machine Learning Research 3 211

    [31]

    Zio E, Maio F D 2012 Expert Systems with Applications 39 10681

    [32]

    Han M, Zhao Y, Yang X L, Lin D 2011 Control Theory and Applications 28 343 (in Chinese) [韩敏, 赵耀, 杨溪林, 林东 2011 控制理论与应用 28 343]

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Publishing process
  • Received Date:  10 January 2013
  • Accepted Date:  24 April 2013
  • Published Online:  05 August 2013

Nonlinear prediction of small scale network traffic based on local relevance vector machine regression model

  • 1. School of Information Science and Engineering, University of Jinan, Jinan 250022, China; Shandong Provincial Key laboratory of Network Based Intelligent Computing, Jinan 250022
Fund Project:  Project supported by the National Natural Science Foundation of China (Grant Nos. 61201428, 61070130, 61173079), the Natural Science Foundation of Shandong Province, China (Grant Nos. ZR2010FQ020, ZR2011FZ003), the Shandong Distinguished Middle-aged and Young Scientist Encourage and Reward Foundation, China (Grant No. BS2009SW003), and the China Postdoctoral Science Foundation (Grant No. 20100470081).

Abstract: Based on the nonlinear time series local prediction method and the relevance vector machine regression model, the local relevance vector machine prediction method is proposed and applied to predict the small scale traffic measurement data, and the BIC-based neighbor point selection method is used to choose the number of nearest-neighbor points for the local relevance vector machine regression model. We also compare the performance of the local relevance vector machine regression model with the feed-forward neural network optimized by particle swarm optimization for the same problem. Experimental results show that the local relevance vector machine prediction method whose neighboring points have been optimized can effectively predict the small scale traffic measurement data, can reproduce the statistical features of real small scale traffic measurements, and the prediction accuracy of the local relevance vector machine regression model is superior to that of the feedforward neural network optimized by PSO and the local linear prediction method.

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