Study on network traffic forecast model of SVR optimized by GAFSA. (English) Zbl 1360.62470
Summary: There are some problems, such as low precision, on existing network traffic forecast model. In accordance with these problems, this paper proposed the network traffic forecast model of support vector regression (SVR) algorithm optimized by global artificial fish swarm algorithm (GAFSA). GAFSA constitutes an improvement of artificial fish swarm algorithm, which is a swarm intelligence optimization algorithm with a significant effect of optimization. The optimum training parameters used for SVR could be calculated by optimizing chosen parameters, which would make the forecast more accurate. With the optimum training parameters searched by GAFSA algorithm, a model of network traffic forecast, which greatly solved problems of great errors in SVR improved by others intelligent algorithms, could be built with the forecast result approaching stability and the increased forecast precision. The simulation shows that, compared with other models (e.g. GA-SVR, CPSO-SVR), the forecast results of GAFSA-SVR network traffic forecast model is more stable with the precision improved to more than 89%, which plays an important role on instructing network control behavior and analyzing security situation.
MSC:
| 62M20 | Inference from stochastic processes and prediction |
| 90B15 | Stochastic network models in operations research |
Keywords:
network traffic forecast; optimization of parameters; support vector regression (SVR); global artificial fish swarm algorithm (GAFSA); self-similaritySoftware:
LIBSVMReferences:
| [1] | Deodatis, G.; Shinozuka, M., Auto-regressive model for nonstationary stochastic processes, J Eng Mech, 114, 11, 1995-2012 (1988) |
| [2] | Wang, W.; Ding, F.; Dai, J., Maximum likelihood least squares identification for systems with autoregressive moving average noise, Appl Math Modell, 36, 5, 1842-1853 (2012) · Zbl 1242.62105 |
| [3] | Asadi, S.; Tavakoli, A.; Hejazi, S. R., A new hybrid for improvement of auto-regressive integrated moving average models applying particle swarm optimization, Expert Syst Appl, 39, 5, 5332-5337 (2012) |
| [4] | Sheng, H.; Chen, Y.; Qiu, T., Fractional autoregressive integrated moving average with stable innovations model of great salt lake elevation time series, Proceedings of the fractional processes and fractional-order signal processing, 179-188 (2012), Springer · Zbl 1245.94004 |
| [5] | Hsu, C.-C.; Chen, C.-Y., Applications of improved grey prediction model for power demand forecasting, Energy Convers Manag, 44, 14, 2241-2249 (2003) |
| [6] | Huang, S.-J.; Huang, C.-L., Control of an inverted pendulum using grey prediction model, Ind Appl. IEEE Trans., 36, 2, 452-458 (2000) |
| [7] | Yu, H.; Liu, J.; Wang, M.; Hu, S.-L.; Guo, R., The trend prediction for spacecraft state based on wavelet analysis and time series method, Proceedings of the 11th international computer conference on wavelet active media technology and information processing (ICCWAMTIP), 2014, 88-91 (2014), IEEE |
| [8] | Li, H.-z.; Guo, S.; Li, C.-j.; Sun, J.-q., A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm, Knowl Based Syst, 37, 378-387 (2013) |
| [9] | Kaastra, I.; Boyd, M., Designing a neural network for forecasting financial and economic time series, Neurocomputing, 10, 3, 215-236 (1996) |
| [10] | Zhang, G. P.; Qi, M., Neural network forecasting for seasonal and trend time series, Eur J Oper Res, 160, 2, 501-514 (2005) · Zbl 1066.62094 |
| [11] | Basak, D.; Pal, S.; Patranabis, D. C., Support vector regression, Neural Inf Process Lett Rev, 11, 10, 203-224 (2007) |
| [12] | Chang, C.-C.; Lin, C.-J., Libsvm: A library for support vector machines, ACM Trans Intell Syst Technol (TIST), 2, 3, 27 (2011) |
| [13] | Kazem, A.; Sharifi, E.; Hussain, F. K.; Saberi, M.; Hussain, O. K., Support vector regression with chaos-based firefly algorithm for stock market price forecasting, Appl Soft Comput, 13, 2, 947-958 (2013) |
| [14] | Kavaklioglu, K., Modeling and prediction of turkeys electricity consumption using support vector regression, Appl Energy, 88, 1, 368-375 (2011) |
| [15] | Hu, J.; Gao, P.; Yao, Y.; Xie, X., Traffic flow forecasting with particle swarm optimization and support vector regression, Proceedings of the IEEE 17th international conference on intelligent transportation systems (ITSC), 2014, 2267-2268 (2014), IEEE |
| [16] | Huang, J.; Bo, Y.; Wang, H., Electromechanical equipment state forecasting based on genetic algorithm-support vector regression, Expert Syst Appl, 38, 7, 8399-8402 (2011) |
| [17] | Chen, K.-Y., Forecasting systems reliability based on support vector regression with genetic algorithms, Reliab Eng Syst Safety, 92, 4, 423-432 (2007) |
| [18] | Lin, S.-W.; Ying, K.-C.; Chen, S.-C.; Lee, Z.-J., Particle swarm optimization for parameter determination and feature selection of support vector machines, Expert Syst Appl, 35, 4, 1817-1824 (2008) |
| [19] | Xu, X.; Zheng, K.; Li, D.; Yang, Y., New chaos-particle swarm optimization algorithm, J Commun, 33, 1, 24-37 (2012) |
| [20] | Neshat, M.; Sepidnam, G.; Sargolzaei, M.; Toosi, A. N., Artificial fish swarm algorithm: a survey of the state-of-the-art, hybridization, combinatorial and indicative applications, Artif Intell Rev, 42, 4, 965-997 (2014) |
| [21] | Wang, H.; Guo, Y., A blind equalization algorithm based on global artificial fish swarm and genetic optimization dna encoding sequences, Proceedings of the 2015 international conference on industrial informatics and computer engineering (2015), Atlantis Press |
| [22] | Leland, W. E.; Taqqu, M. S.; Willinger, W.; Wilson, D. V., On the self-similar nature of ethernet traffic (extended version), Netw IEEE/ACM Trans, 2, 1, 1-15 (1994) |
| [23] | Paxson, V.; Floyd, S., Wide area traffic: the failure of poisson modeling, IEEE/ACM Trans Netw (ToN), 3, 3, 226-244 (1995) |
| [24] | Clegg, R. G., A practical guide to measuring the Hurst parameter, Int J Simul Syst, Sci Technol, 7, 2, 3-14 (2006) |
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