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基于非线性时间序列局域预测法与相关向量机回归模型, 本文提出了局域相关向量机预测方法, 并应用于预测实际的小尺度网路流量序列. 应用基于信息准则的局域预测法邻近点的选取方法来选取局域相关向量机回归模型的邻近点个数. 对比分析了局域相关向量机预测法、前馈神经网络模型与局域线性预测法对网络流量序列的预测性能, 其中前馈神经网络模型的参数采用粒子群优化算法来优化. 实验结果表明: 邻近点优化后的局域相关向量机回归模型能够有效地预测小尺度网络流量序列, 归一化均方误差很小; 局域相关向量机回归模型生成的时间序列具有与原网络流量时间序列相一致的概率分布; 局域相关向量机回归模型的预测精度好于前馈神经网络模型的与局域线性预测法的.
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关键词:
- 小尺度网络流量 /
- 非线性时间序列预测方法 /
- 局域预测法 /
- 相关向量机回归模型
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.-
Keywords:
- small scale network traffic /
- nonlinear time series prediction method /
- local prediction method /
- relevance vector machine regression model
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[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
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[10] Karunasinghe D S K, Liong S Y 2006 Journal of Hydrology 323 92
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[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
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[19] Meng Q F, Peng Y H 2007 Phys. Lett. A 370 465
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[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 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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[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 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 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 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 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 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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