Open AccessAutoregressive models of network traffic prediction
Author(s) -
Т. М. Татарникова,
B. Ya. Sovetov,
V. Chehanovsky
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1864/1/012099
Subject(s) - autoregressive model , computer science , construct (python library) , relevance (law) , traffic generation model , node (physics) , data mining , class (philosophy) , artificial intelligence , econometrics , real time computing , computer network , mathematics , engineering , structural engineering , political science , law
The relevance of the network traffic prediction is due to the requirement to store a large amount of data for a long time. In this regard, it is necessary to predict traffic volumes in order to take the necessary measures to protect and preserve data. The paper substantiates the use of autoregressive class models to construct predictive estimates. The results allowing substantiating the prospective requirements for the memory volumes of the node equipment of the infocommunication network are given.