Research on short-term flow prediction of local area network based on ARMA model

Yiyong Lin; Yang Qiao; Shibin Hu; Bingxi Dong; Xinghua Zhang

doi:10.1117/12.2640665

15 July 2022 Research on short-term flow prediction of local area network based on ARMA model

Yiyong Lin, Yang Qiao, Shibin Hu, Bingxi Dong, Xinghua Zhang

Proceedings Volume 12258, International Conference on Neural Networks, Information, and Communication Engineering (NNICE 2022); 122581E (2022) https://doi.org/10.1117/12.2640665
Event: International Conference on Neural Networks, Information, and Communication Engineering (NNICE 2022), 2022, Qingdao, China

Abstract

The characteristics of the ARMA model are analyzed according to the characteristics of the short-term flow data of the local area network in this paper. The time prediction model of network flow is established on the ARMA method. The prediction parameters of the ARMA model are determined and the model is simulated According to the short-term flow prediction. The comparison between the simulation results and the measured data of NetFlow shows that the model can accurately predict the short-term flow behavior trend of the local area network, which can provide reference and reference for the analysis of network traffic behavior.

Citation Download Citation

Yiyong Lin, Yang Qiao, Shibin Hu, Bingxi Dong, and Xinghua Zhang "Research on short-term flow prediction of local area network based on ARMA model", Proc. SPIE 12258, International Conference on Neural Networks, Information, and Communication Engineering (NNICE 2022), 122581E (15 July 2022); https://doi.org/10.1117/12.2640665

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