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Traffic Flows Forecasting Based on Machine Learning

Traffic Flows Forecasting Based on Machine Learning

Vladimir Deart, Vladimir Mankov, Irina Krasnova
Copyright: © 2022 |Volume: 13 |Issue: 1 |Pages: 19
ISSN: 1947-3176|EISSN: 1947-3184|EISBN13: 9781683181774|DOI: 10.4018/IJERTCS.289198
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MLA

Deart, Vladimir, et al. "Traffic Flows Forecasting Based on Machine Learning." IJERTCS vol.13, no.1 2022: pp.1-19. http://doi.org/10.4018/IJERTCS.289198

APA

Deart, V., Mankov, V., & Krasnova, I. (2022). Traffic Flows Forecasting Based on Machine Learning. International Journal of Embedded and Real-Time Communication Systems (IJERTCS), 13(1), 1-19. http://doi.org/10.4018/IJERTCS.289198

Chicago

Deart, Vladimir, Vladimir Mankov, and Irina Krasnova. "Traffic Flows Forecasting Based on Machine Learning," International Journal of Embedded and Real-Time Communication Systems (IJERTCS) 13, no.1: 1-19. http://doi.org/10.4018/IJERTCS.289198

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Abstract

The article aims to develop a model for forecasting the characteristics of traffic flows in real-time based on the classification of applications using machine learning methods to ensure the quality of service. It is shown that the model can forecast the mean rate and frequency of packet arrival for the entire flow of each class separately. The prediction is based on information about the previous flows of this class and the first 15 packets of the active flow. Thus, the Random Forest Regression method reduces the prediction error by approximately 1.5 times compared to the standard mean estimate for transmitted packets issued at the switch interface.

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