Cortez et al 2007a - Revision history

Scipediacontent at 02:47, 2 February 2021

2021-02-02T02:47:45Z

Scipediacontent: Scipediacontent moved page Draft Content 130790406 to Cortez et al 2007a

2020-10-14T12:54:35Z

Scipediacontent moved page Draft Content 130790406 to Cortez et al 2007a

Scipediacontent: Created page with " == Abstract == Forecasting Internet traffic is receiving an increasing attention from the computer networks domain. Indeed, by improving this task efficient traffic engineer..."

2020-10-14T12:54:33Z

Created page with " == Abstract == Forecasting Internet traffic is receiving an increasing attention from the computer networks domain. Indeed, by improving this task efficient traffic engineer..."

New page

== Abstract ==

Forecasting Internet traffic is receiving an increasing attention from the computer networks domain. Indeed, by improving this task efficient traffic engineering and anomaly detection tools can be developed, leading to economic gains due to better resource management. This paper presents a Neural Network (NN) approach to predict TCP/IP traffic for all links of a backbone network, using both univariate and multivariate strategies. The former uses only past values of the forecasted link, while the latter is based on the neighbor links of the backbone topology. Several experiments were held by considering real-world data from the UK education and research network. Also, different time scales (e.g. every ten minutes and hourly) were analyzed. Overall, the proposed NN approach outperformed other forecasting methods (e.g. Holt-Winters). R&D Algoritmi centre

Document type: Part of book or chapter of book

== Full document ==
<pdf>Media:Draft_Content_130790406-beopen660-7481-document.pdf</pdf>

== Original document ==

The different versions of the original document can be found in:

* [http://hdl.handle.net/1822/7634 http://hdl.handle.net/1822/7634]

* [http://repositorium.sdum.uminho.pt/bitstream/1822/7634/1/forecast.pdf http://repositorium.sdum.uminho.pt/bitstream/1822/7634/1/forecast.pdf]

← Older revision		Revision as of 02:47, 2 February 2021
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	Forecasting Internet traffic is receiving an increasing attention from the computer networks domain. Indeed, by improving this task efficient traffic engineering and anomaly detection tools can be developed, leading to economic gains due to better resource management. This paper presents a Neural Network (NN) approach to predict TCP/IP traffic for all links of a backbone network, using both univariate and multivariate strategies. The former uses only past values of the forecasted link, while the latter is based on the neighbor links of the backbone topology. Several experiments were held by considering real-world data from the UK education and research network. Also, different time scales (e.g. every ten minutes and hourly) were analyzed. Overall, the proposed NN approach outperformed other forecasting methods (e.g. Holt-Winters). R&D Algoritmi centre		Forecasting Internet traffic is receiving an increasing attention from the computer networks domain. Indeed, by improving this task efficient traffic engineering and anomaly detection tools can be developed, leading to economic gains due to better resource management. This paper presents a Neural Network (NN) approach to predict TCP/IP traffic for all links of a backbone network, using both univariate and multivariate strategies. The former uses only past values of the forecasted link, while the latter is based on the neighbor links of the backbone topology. Several experiments were held by considering real-world data from the UK education and research network. Also, different time scales (e.g. every ten minutes and hourly) were analyzed. Overall, the proposed NN approach outperformed other forecasting methods (e.g. Holt-Winters). R&D Algoritmi centre
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−	~~Document type: Part of book or chapter of book~~
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−	~~== Full document ==~~
−	~~<pdf>Media:Draft_Content_130790406-beopen660-7481-document.pdf</pdf>~~


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	* [http://repositorium.sdum.uminho.pt/bitstream/1822/7634/1/forecast.pdf http://repositorium.sdum.uminho.pt/bitstream/1822/7634/1/forecast.pdf]		* [http://repositorium.sdum.uminho.pt/bitstream/1822/7634/1/forecast.pdf http://repositorium.sdum.uminho.pt/bitstream/1822/7634/1/forecast.pdf]
		+
		+	* [http://link.springer.com/content/pdf/10.1007/978-3-540-74695-9_46 http://link.springer.com/content/pdf/10.1007/978-3-540-74695-9_46],
		+	: [http://dx.doi.org/10.1007/978-3-540-74695-9_46 http://dx.doi.org/10.1007/978-3-540-74695-9_46]
		+
		+	* [https://repositorium.sdum.uminho.pt/bitstream/1822/7634/1/forecast.pdf https://repositorium.sdum.uminho.pt/bitstream/1822/7634/1/forecast.pdf],
		+	: [https://link.springer.com/chapter/10.1007/978-3-540-74695-9_46 https://link.springer.com/chapter/10.1007/978-3-540-74695-9_46],
		+	: [https://dblp.uni-trier.de/db/conf/icann/icann2007-2.html#CortezRSR07 https://dblp.uni-trier.de/db/conf/icann/icann2007-2.html#CortezRSR07],
		+	: [https://www.scipedia.com/public/Cortez_et_al_2007a https://www.scipedia.com/public/Cortez_et_al_2007a],
		+	: [http://repositorium.sdum.uminho.pt/handle/1822/7634 http://repositorium.sdum.uminho.pt/handle/1822/7634],
		+	: [https://dl.acm.org/citation.cfm?id=1778118 https://dl.acm.org/citation.cfm?id=1778118],
		+	: [http://discovery.ucl.ac.uk/44052 http://discovery.ucl.ac.uk/44052],
		+	: [https://rd.springer.com/chapter/10.1007/978-3-540-74695-9_46 https://rd.springer.com/chapter/10.1007/978-3-540-74695-9_46],
		+	: [https://academic.microsoft.com/#/detail/1792912906 https://academic.microsoft.com/#/detail/1792912906]
		+
		+	* [ ]

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