Network Traffic Prediction using ARIMA and Neural Networks Models
The network traffic prediction plays a fundamental role in network design, management, control and optimization. The self-similar and non-linear nature of traffic makes accurate prediction more difficult. This paper presents a view of models used for network traffic prediction using ARIMA and neural network models. Our experiments inspect the accuracy of k-step ahead prediction using ARIMA and neural network models. Ill. 11, bibl. 18 (in English; summaries in English, Russian and Lithuanian).
How to Cite
The copyright for the paper in this journal is retained by the author(s) with the first publication right granted to the journal. The authors agree to the Creative Commons Attribution 4.0 (CC BY 4.0) agreement under which the paper in the Journal is licensed.
By virtue of their appearance in this open access journal, papers are free to use with proper attribution in educational and other non-commercial settings with an acknowledgement of the initial publication in the journal.