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Article

Modeling and Prediction of Daily Traffic Patterns—WASK and SIX Case Study

Department of Systems and Computer Networks, Faculty of Electronics, Wroclaw University of Science and Technology, Wyb. Wyspianskiego 27, 50-370 Wroclaw, Poland
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Academic Editors: Alexey Vinel and Paul Mitchell
Electronics 2021, 10(14), 1637; https://doi.org/10.3390/electronics10141637
Received: 20 May 2021 / Revised: 30 June 2021 / Accepted: 7 July 2021 / Published: 9 July 2021
(This article belongs to the Special Issue Telecommunication Networks)
The paper studies efficient modeling and prediction of daily traffic patterns in transport telecommunication networks. The investigation is carried out using two historical datasets, namely WASK and SIX, which collect flows from edge nodes of two networks of different size. WASK is a novel dataset introduced and analyzed for the first time in this paper, while SIX is a well-known source of network flows. For the considered datasets, the paper proposes traffic modeling and prediction methods. For traffic modeling, the Fourier Transform is applied. For traffic prediction, two approaches are proposed—modeling-based (the forecasting model is generated based on historical traffic models) and machine learning-based (network traffic is handled as a data stream where chunk-based regression methods are applied for forecasting). Then, extensive simulations are performed to verify efficiency of the approaches and their comparison. The proposed modeling method revealed high efficiency especially for the SIX dataset, where the average error was lower than 0.1%. The efficiency of two forecasting approaches differs with datasets–modeling-based methods achieved lower errors for SIX while machine learning-based for WASK. The average prediction error for SIX reached 3.36% while forecasting for WASK turned out extremely challenging. View Full-Text
Keywords: traffic modeling; traffic prediction; network modeling; Fourier Transform; machine learning traffic modeling; traffic prediction; network modeling; Fourier Transform; machine learning
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MDPI and ACS Style

Goścień, R.; Knapińska, A.; Włodarczyk, A. Modeling and Prediction of Daily Traffic Patterns—WASK and SIX Case Study. Electronics 2021, 10, 1637. https://doi.org/10.3390/electronics10141637

AMA Style

Goścień R, Knapińska A, Włodarczyk A. Modeling and Prediction of Daily Traffic Patterns—WASK and SIX Case Study. Electronics. 2021; 10(14):1637. https://doi.org/10.3390/electronics10141637

Chicago/Turabian Style

Goścień, Róża, Aleksandra Knapińska, and Adam Włodarczyk. 2021. "Modeling and Prediction of Daily Traffic Patterns—WASK and SIX Case Study" Electronics 10, no. 14: 1637. https://doi.org/10.3390/electronics10141637

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