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Multiple time-series forecasting on mobile network data using an RNN-RBM model
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
2017 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

The purpose of this project is to evaluate the performance of a forecasting model based on a multivariate dataset consisting of time series of traffic characteristic performance data from a mobile network. The forecasting is made using machine learning with a deep neural network. The first part of the project involves the adaption of the model design to fit the dataset and is followed by a number of simulations where the aim is to tune the parameters of the model to give the best performance. The simulations show that with well tuned parameters, the neural network performes better than the baseline model, even when using only a univariate dataset. If a multivariate dataset is used, the neural network outperforms the baseline model even when the dataset is small.

Place, publisher, year, edition, pages
2017. , p. 57
Series
UPTEC F, ISSN 1401-5757 ; 17005
Keywords [en]
Machine Learning, Artificiell Intelligens, RNN, RBM RNN-RBM, Deep Learning, Multivariate
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:uu:diva-315782OAI: oai:DiVA.org:uu-315782DiVA, id: diva2:1075835
External cooperation
Ericsson AB
Educational program
Master Programme in Engineering Physics
Supervisors
Examiners
Available from: 2017-02-21 Created: 2017-02-21 Last updated: 2017-02-21Bibliographically approved

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • 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