基于FARIMA-GARCH模型的网络业务预测算法

doi:10.3969/j.issn.1000-436x.2013.03.004

Abstract

Abstract:

The volatility and self-similarity features of network traffic poses great cha lenge to network traffic prediction. For this purpose, a novel network traffic prediction scheme based on FARIMA-GARCH model was formulated. A novel method was used to get a zero-mean traffic series by a piecewise two-way CUSUM detection algorithm. Then the fraction difference order was evaluated with precision by the presented bounded search method. After obtaining the model para-meters, the prediction algorithm was conducted by using FARIMA-GARCH model. Compared with the traditional me-thod, the limited search method reduces the evaluated error despite a slight computational cost. Then simulation was car-ried out to verify the accuracy of proposed algorithm with real network traffic. The proposed prediction method keeps the same time complexity with the FARIMA model prediction method, and the simulation result shows that the root mean-square error and relative root mean-square error, which closely resemble the RBF prediction method, is less than FARIMA model prediction method. And the interval estimation and volatility prediction performance is excellent.

Key words: FARIMA, GARCH, CUSUM, traffic prediction

Shuang-mao YANG,Wei GUO,Wei TANG. Network traffic prediction based on FARIMA-GARCH model[J]. Journal on Communications, 2013, 34(3): 23-31.

Figures/Tables 15

References 35

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H	方差法	R/S分析法	A-V小波法	自适应法	限定搜索法
0.55	0.548 5(0.56s)	0.499 7(56.66s)	0.547 0(0.16s)	0.550 2(0.27s)	0.549 4(4.25s)
0.60	0.572 0(0.48s)	0.544 1(58.88s)	0.590 4(0.10s)	0.594 1(0.25s)	0.600 5(5.17s)
0.70	0.708 0(0.52s)	0.693 2(57.17s)	0.689 3(0.07s)	0.694 6(0.25s)	0.699 9(4.74s)
0.80	0.720 2(0.53s)	0.757 9(57.77s)	0.790 2(0.11s)	0.794 2(0.20s)	0.799 6(4.72s)
0.85	0.839 9(0.53s)	0.856 3(58.03s)	0.864 6(0.09s)	0.854 3(0.20s)	0.849 5(5.25s)
0.90	0.858 7(0.52s)	0.914 6(56.75s)	0.926 8(0.11s)	0.913 1(0.26s)	0.900 3(5.66s)
0.95	0.885 9(0.49s)	0.923 5(57.36s)	0.931 9(0.09s)	0.943 3(0.234s)	0.950 8(4.23s)

业务流名称	H指数	均值	方差
sig04_ver01(10ms)	0.649 2	0.665 1	2.183 4
sig04_ver01(100ms)	0.831 5	6.648 9	31.069 0
sig04_ver02(10ms)	0.717 2	2.034 6	10.545 8
sig04_ver02(100ms)	0.837 5	20.325 2	206.875 7
sig04_ver03(10ms)	0.758 5	1.681 0	8.545 8
sig04_ver03(100ms)	0.853 9	16.806 7	187.432 3

预测算法及其评价指标	单步预测	5步预测	15步预测	25步预测
RMSE(FARIMA)	1.628 6	5.314 2	7.704 5	9.476 0
RMSE(RBF)	1.998 8	1.980 6	1.958	2.999 6
RMSE(FARIMA-GARCH)	1.568 5	1.874 9	2.757 3	3.302 7
RRMSE(FARIMA)	1.101 3	3.565 7	5.479 1	6.536 1
RRMSE(RBF)	1.062 32	1.573 8	1.804	2.106 3
RRMSE(FARIMA-GARCH)	1.026	2.646 8	4.337 4	5.272 5
IFA(FARIMA)	95.51%	73.90%	55.92%	48.30%
IFA(FARIMA-GARCH)	96.61%	90.59%	80.92%	75.23%

预测算法及其评价指标	单步预测	5步预测	15步预测	25步预测
RMSE(FARIMA)	1.791 1	2.630 8	2.865 7	2.879 3
RMSE(RBF)	2.357 5	2.365 7	2.349 8	2.315 8
RMSE(FARIMA-GARCH)	1.702 4	1.753 6	1.960 8	2.285 1
RRMSE(FARIMA)	1.472 8	2.525 7	2.857 5	2.879 6
RRMSE(RBF)	1.031 1	1.119 4	1.306 8	1.295 7
RRMSE(FARIMA-GARCH)	1.114 7	1.400 0	1.697 2	2.569 8
IFA(FARIMA)	95.22%	91.96%	89.93%	84.13%
IFA(FARIMA-GARCH)	97.96%	96.44%	95.73%	95.52%

预测算法及其评价指标	单步预测	5步预测	15步预测	25步预测
RMSE(FARIMA)	3.606 4	5.355 3	8.852 6	9.157 4
RMSE(RBF)	5.057 7	5.033 6	4.901 7	4.858 5
RMSE(FARIMA-GARCH)	3.399 4	4.124 1	4.276 9	4.438 0
RRMSE(FARIMA)	1.664 3	4.207 4	7.152 2	7.839 3
RRMSE(RBF)	1.301 0	1.480 1	1.874 8	1.915 3
RRMSE(FARIMA-GARCH)	0.941 1	2.148 2	2.256 5	2.821 9
IFA(FARIMA)	85.73%	67.93%	55.08%	52.56%
IFA(FARIMA-GARCH)	92.53%	91.18%	90.01%	87.15%

Network traffic prediction based on FARIMA-GARCH model

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预测算法及其评价指标	单步预测	5步预测	15步预测	25步预测
RMSE(FARIMA)	7.706 1	8.410 8	10.471 9	11.411 4
RMSE(RBF)	9.597 6	10.352 5	10.323 7	10.633 8
RMSE(FARIMA-GARCH)	4.792 0	8.238 2	9.914 0	10.413 5
RRMSE(FARIMA)	0.863 6	1.226 4	1.493 5	1.552 4
RRMSE(RBF)	1.803 0	1.965 9	1.925 7	2.005 2
RRMSE(FARIMA-GARCH)	0.697 3	0.949 8	1.139 4	1.389 2
IFA(FARIMA)	92.72%	75.83%	68.94%	62.65%
IFA(FARIMA-GARCH)	93.38%	91.72%	90.07%	87.09%

预测算法及其评价指标	单步预测	5步预测	15步预测	25步预测
RMSE(FARIMA)	8.895 8	15.014 6	14.132 2	16.493 7
RMSE(RBF)	12.498 8	12.112 9	12.481 3	11.002 8
RMSE(FARIMA-GARCH)	2.546 4	3.488 1	4.554 8	9.192 1
RRMSE(FARIMA)	0.718 9	1.806 7	1.684 4	2.205 4
RRMSE(RBF)	0.567 8	0.541 0	0.811 0	0.965 5
RRMSE(FARIMA-GARCH)	0.284 8	0.618 0	1.136 2	1.205 3
IFA(FARIMA)	99.15%	88.90%	90.18%	80.56%
IFA(FARIMA-GARCH)	99.24%	96.13%	96.50%	96.20%

预测算法及其评价指标	单步预测	5步预测	15步预测	25步预测
RMSE(FARIMA)	17.833 7	24.817 0	26.780 0	25.904 8
RMSE(RBF)	19.589 5	19.777 35	20.069 6	19.808 6
RMSE(FARIMA-GARCH)	16.102 7	21.031 8	24.646 7	23.620 8
RRMSE(FARIMA)	0.667 6	0.917 8	1.020 4	0.996 7
RRMSE(RBF)	0.774 4	0.949 2	0.971 2	0.965 5
RRMSE(FARIMA-GARCH)	0.473 1	0.739 2	0.970 6	0.936 4
IFA(FARIMA)	76.47%	68.91%	61.50%	52.94%
IFA(FARIMA-GARCH)	89.08%	75.63%	60.50%	63.87%