Network traffic analysis and bandwidth forecasting for using Meta's Prophet: a case study.
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| Title: | Network traffic analysis and bandwidth forecasting for using Meta's Prophet: a case study. |
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| Authors: | Isaac, Yusuf Onimisi1 yusuf.isaac@lmu.edu.ng, Bamisaye, Ayodeji James2 ayobamisaye@gmail.com, Adedotun, Ijagbemi1 ijagbemi.adedotun@lmu.edu.ng, Dada, Theophilus Olusegun1 dada.theophilus@lmu.edu.ng, John, Onyemenam Obiajulu1 onyemenam.obiajulu@lmu.edu.ng |
| Source: | Telkomnika. Jun2026, Vol. 24 Issue 3, p751-764. 14p. |
| Subjects: | Internet traffic, Forecasting, Forecasting methodology, Computer network traffic, Quality of service, Student housing, Machine learning |
| Abstract: | This study created a forward-looking bandwidth prediction system for students' halls of residence at Landmark University. The system uses Meta's Prophet, a method for analyzing patterns in data over time, and was trained on past internet traffic data from October to December 2024. The system was able to predict future bandwidth usage with over 90% accuracy. To assess how well the system worked, several common metrics were used, including mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE). The MAE was calculated as 10,099,863.10 bits per second (bps), and the RMSE was 13,570,959.58 bps. While the mean squared error (MSE) appears large numerically, this is anticipated due to the size of the bandwidth data involved in its calculation. Importantly, the prediction errors are considered reasonable when considered in relation to the actual peak bandwidth usage, which fluctuated between 47 and 50 megabits per second (Mbps). These findings suggest that machine learning can be a valuable tool for refining network infrastructure and improving the user experience quality of service (QoS) in environments with many users, such as university residences. [ABSTRACT FROM AUTHOR] |
| Copyright of Telkomnika is the property of Department of Electrical Engineering, Ahmad Dahlan University and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 195172765 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Network traffic analysis and bandwidth forecasting for using Meta's Prophet: a case study. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Isaac%2C+Yusuf+Onimisi%22">Isaac, Yusuf Onimisi</searchLink><relatesTo>1</relatesTo><i> yusuf.isaac@lmu.edu.ng</i><br /><searchLink fieldCode="AR" term="%22Bamisaye%2C+Ayodeji+James%22">Bamisaye, Ayodeji James</searchLink><relatesTo>2</relatesTo><i> ayobamisaye@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Adedotun%2C+Ijagbemi%22">Adedotun, Ijagbemi</searchLink><relatesTo>1</relatesTo><i> ijagbemi.adedotun@lmu.edu.ng</i><br /><searchLink fieldCode="AR" term="%22Dada%2C+Theophilus+Olusegun%22">Dada, Theophilus Olusegun</searchLink><relatesTo>1</relatesTo><i> dada.theophilus@lmu.edu.ng</i><br /><searchLink fieldCode="AR" term="%22John%2C+Onyemenam+Obiajulu%22">John, Onyemenam Obiajulu</searchLink><relatesTo>1</relatesTo><i> onyemenam.obiajulu@lmu.edu.ng</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Telkomnika%22">Telkomnika</searchLink>. Jun2026, Vol. 24 Issue 3, p751-764. 14p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Internet+traffic%22">Internet traffic</searchLink><br /><searchLink fieldCode="DE" term="%22Forecasting%22">Forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Forecasting+methodology%22">Forecasting methodology</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+network+traffic%22">Computer network traffic</searchLink><br /><searchLink fieldCode="DE" term="%22Quality+of+service%22">Quality of service</searchLink><br /><searchLink fieldCode="DE" term="%22Student+housing%22">Student housing</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This study created a forward-looking bandwidth prediction system for students' halls of residence at Landmark University. The system uses Meta's Prophet, a method for analyzing patterns in data over time, and was trained on past internet traffic data from October to December 2024. The system was able to predict future bandwidth usage with over 90% accuracy. To assess how well the system worked, several common metrics were used, including mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE). The MAE was calculated as 10,099,863.10 bits per second (bps), and the RMSE was 13,570,959.58 bps. While the mean squared error (MSE) appears large numerically, this is anticipated due to the size of the bandwidth data involved in its calculation. Importantly, the prediction errors are considered reasonable when considered in relation to the actual peak bandwidth usage, which fluctuated between 47 and 50 megabits per second (Mbps). These findings suggest that machine learning can be a valuable tool for refining network infrastructure and improving the user experience quality of service (QoS) in environments with many users, such as university residences. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Telkomnika is the property of Department of Electrical Engineering, Ahmad Dahlan University and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.12928/TELKOMNIKA.v24i3.27609 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 751 Subjects: – SubjectFull: Internet traffic Type: general – SubjectFull: Forecasting Type: general – SubjectFull: Forecasting methodology Type: general – SubjectFull: Computer network traffic Type: general – SubjectFull: Quality of service Type: general – SubjectFull: Student housing Type: general – SubjectFull: Machine learning Type: general Titles: – TitleFull: Network traffic analysis and bandwidth forecasting for using Meta's Prophet: a case study. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Isaac, Yusuf Onimisi – PersonEntity: Name: NameFull: Bamisaye, Ayodeji James – PersonEntity: Name: NameFull: Adedotun, Ijagbemi – PersonEntity: Name: NameFull: Dada, Theophilus Olusegun – PersonEntity: Name: NameFull: John, Onyemenam Obiajulu IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 16936930 Numbering: – Type: volume Value: 24 – Type: issue Value: 3 Titles: – TitleFull: Telkomnika Type: main |
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