Forecasting key performance indicator of mobile networks: application to mobile cellular networks
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10773/29563 |
Resumo: | The increase of data trafic in the world has increased the need for mobile network operators to take greater care in planning and managing theirs infrastructures. This work explores the performance of several statistical forecasting models aplied in voice and data trafic. This data was obtained from an European mobile network, and, regarding the predictive models, it was applied classic models like exponential smoothing, Holt-Winters, ARIMA, Random-Walk; as well as two more recent model proposals. Regarding the daily data, the proposed model could predict values with higher precision compared to the other models. For hourly data, depending on the time zone where the models were tested, the models with higher performance were Random-Walk and the second proposed model. In summary, this dissertation shows the performance of several classic statistical models, and how these compare to recently proposed models. It also shows that mobile network operator can use statistical forecasting methods to try to get information on how their network might react in future, giving valueable insights to perform a better managment of their network. |
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Forecasting key performance indicator of mobile networks: application to mobile cellular networksMobile networksForecastingExponential SmoothingKPITime SeriesARIMARandom-WalkThe increase of data trafic in the world has increased the need for mobile network operators to take greater care in planning and managing theirs infrastructures. This work explores the performance of several statistical forecasting models aplied in voice and data trafic. This data was obtained from an European mobile network, and, regarding the predictive models, it was applied classic models like exponential smoothing, Holt-Winters, ARIMA, Random-Walk; as well as two more recent model proposals. Regarding the daily data, the proposed model could predict values with higher precision compared to the other models. For hourly data, depending on the time zone where the models were tested, the models with higher performance were Random-Walk and the second proposed model. In summary, this dissertation shows the performance of several classic statistical models, and how these compare to recently proposed models. It also shows that mobile network operator can use statistical forecasting methods to try to get information on how their network might react in future, giving valueable insights to perform a better managment of their network.O aumento do tráfego de dados no mundo, aumentou a necessidade dos operadores de redes móveis terem um maior cuidado a planear e gerir as suas infraestruturas. Este trabalho explora o desempenho de vários modelos estatísticos de previsão aplicados a tráfego de voz e de dados. Os dados têm origem numa rede móvel Europeia. Relativamente aos modelos preditivos, foram aplicados modelos clássicos como alisamento exponencial, Holt-Winters, ARIMA, Random-Walk; bem como duas propostas de modelos mais recentes. Em suma, esta dissertação mostra o desempenho de alguns modelos estatísticos clássicos, e como estes se comparam a modelos recentemente propostos. Também mostra que operadores de redes podem usar métodos preditivos estatísticos para tentar obter informações de como a sua rede pode reagir no futuro, dando assim informações valiosas para que estes efetuem uma melhor gestão da sua rede.2020-10-22T14:41:46Z2019-07-01T00:00:00Z2019-07info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10773/29563engSousa, Ângelo Miguel Raposo Almeida einfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-02-22T11:57:13Zoai:ria.ua.pt:10773/29563Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:01:52.430669Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Forecasting key performance indicator of mobile networks: application to mobile cellular networks |
title |
Forecasting key performance indicator of mobile networks: application to mobile cellular networks |
spellingShingle |
Forecasting key performance indicator of mobile networks: application to mobile cellular networks Sousa, Ângelo Miguel Raposo Almeida e Mobile networks Forecasting Exponential Smoothing KPI Time Series ARIMA Random-Walk |
title_short |
Forecasting key performance indicator of mobile networks: application to mobile cellular networks |
title_full |
Forecasting key performance indicator of mobile networks: application to mobile cellular networks |
title_fullStr |
Forecasting key performance indicator of mobile networks: application to mobile cellular networks |
title_full_unstemmed |
Forecasting key performance indicator of mobile networks: application to mobile cellular networks |
title_sort |
Forecasting key performance indicator of mobile networks: application to mobile cellular networks |
author |
Sousa, Ângelo Miguel Raposo Almeida e |
author_facet |
Sousa, Ângelo Miguel Raposo Almeida e |
author_role |
author |
dc.contributor.author.fl_str_mv |
Sousa, Ângelo Miguel Raposo Almeida e |
dc.subject.por.fl_str_mv |
Mobile networks Forecasting Exponential Smoothing KPI Time Series ARIMA Random-Walk |
topic |
Mobile networks Forecasting Exponential Smoothing KPI Time Series ARIMA Random-Walk |
description |
The increase of data trafic in the world has increased the need for mobile network operators to take greater care in planning and managing theirs infrastructures. This work explores the performance of several statistical forecasting models aplied in voice and data trafic. This data was obtained from an European mobile network, and, regarding the predictive models, it was applied classic models like exponential smoothing, Holt-Winters, ARIMA, Random-Walk; as well as two more recent model proposals. Regarding the daily data, the proposed model could predict values with higher precision compared to the other models. For hourly data, depending on the time zone where the models were tested, the models with higher performance were Random-Walk and the second proposed model. In summary, this dissertation shows the performance of several classic statistical models, and how these compare to recently proposed models. It also shows that mobile network operator can use statistical forecasting methods to try to get information on how their network might react in future, giving valueable insights to perform a better managment of their network. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-07-01T00:00:00Z 2019-07 2020-10-22T14:41:46Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10773/29563 |
url |
http://hdl.handle.net/10773/29563 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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