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Multiple Time Series Forecasting of Cellular Network Traffic
Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

The mobile traffic in cellular networks is increasing in a steady rate as we go intoa future where we are connected to the internet practically all the time in one wayor another. To map the mobile traffic and the volume pressure on the base stationduring different time periods, it is useful to have the ability to predict the trafficvolumes within cellular networks. The data in this work consists of 4G cellular trafficdata spanning over a 7 day coherent period, collected from cells in a moderately largecity. The proposed method in this work is ARIMA modeling, in both original formand with an extension where the coefficients of the ARIMA model are re-esimated byintroducing some user characteristic variables. The re-estimated coefficients produceslightly lower forecast errors in general than a isolated ARIMA model where thevolume forecasts only depends on time. This implies that the forecasts can besomewhat improved when we allow the influence of these variables to be a part ofthe model, and not only the time series itself.

Place, publisher, year, edition, pages
2019. , p. 61
Keywords [en]
time series analysis, cellular networks, traffic load, arima, sarima, forecasting, machine learning, statistics, load prediction
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:liu:diva-154868ISRN: LIU-IDA/STAT-A--19/004—SEOAI: oai:DiVA.org:liu-154868DiVA, id: diva2:1292982
External cooperation
Ericsson
Subject / course
Statistics
Supervisors
Examiners
Available from: 2019-03-05 Created: 2019-03-01 Last updated: 2019-03-05Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf