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Forecasting for Network Management with Joint Statistical Modelling and Machine Learning

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dc.affiliation.dptoUC3M. Departamento de Ingeniería Telemáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Network Technologieses
dc.contributor.authorLo Schiavo, Leonardo
dc.contributor.authorFiore, Marco
dc.contributor.authorGramaglia, Marco
dc.contributor.authorBanchs Roca, Albert
dc.contributor.authorCosta-Pérez, Xavier
dc.contributor.funderEuropean Commissiones
dc.contributor.funderMinisterio de Asuntos Económicos y Transformación Digital (España)es
dc.date.accessioned2022-10-28T09:49:40Z
dc.date.available2022-10-28T09:49:40Z
dc.date.issued2022-06-14
dc.description.abstractForecasting is a task of ever increasing importance for the operation of mobile networks, where it supports anticipa tory decisions by network intelligence and enables emerging zero touch service and network management models. While current trends in forecasting for anticipatory networking lean towards the systematic adoption of models that are purely based on deep learning approaches, we pave the way for a different strategy to the design of predictors for mobile network environments. Specifically, following recent advances in time series prediction, we consider a hybrid approach that blends statistical modelling and machine learning by means of a joint training process of the two methods. By tailoring this mixed forecasting engine to the specific requirements of network traffic demands, we develop a Thresholded Exponential Smoothing and Recurrent Neural Network (TES-RNN) model. We experiment with TES RNN in two practical network management use cases, i.e., (i) anticipatory allocation of network resources, and (ii) mobile traffic anomaly prediction. Results obtained with extensive traffic workloads collected in an operational mobile network show that TES-RNN can yield substantial performance gains over current state-of-the-art predictors in both applications considereden
dc.description.sponsorshipThis work is partially supported by the European Union's Horizon 2020 research and innovation programme under grant agreement no.101017109 DAEMON. This work is partially supported by the Spanish Ministry of Economic Affairs and Digital Transformation and the European Union-NextGenerationEU through the UNICO 5G I+D 6GCLARION-OR and AEON-ZERO. The authors would like to thank Dario Bega for his contribution to developing the forecasting use case I, and Slawek Smyl for his feedback on the baseline ES-RNN model.en
dc.format.extent10
dc.identifier.bibliographicCitationL. Lo Schiavo, M. Fiore, M. Gramaglia, A. Banchs and X. Costa-Perez, "Forecasting for Network Management with Joint Statistical Modelling and Machine Learning," 2022 IEEE 23rd International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), 2022, pp. 60-69en
dc.identifier.doihttps://doi.org/10.1109/WoWMoM54355.2022.00028
dc.identifier.isbn978-1-6654-0876-9
dc.identifier.publicationfirstpage60
dc.identifier.publicationlastpage69
dc.identifier.urihttps://hdl.handle.net/10016/35948
dc.identifier.uxxiCC/0000033565
dc.language.isoeng
dc.publisherIEEEen
dc.relation.eventdate2022-06-14
dc.relation.eventplaceReino Unidoes
dc.relation.eventtitle23rd IEEE International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM), 2022en
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/101017109en
dc.relation.projectIDGobierno de España. TSI-063000-2021-66es
dc.rights© 2022 IEEEen
dc.rights.accessRightsopen accessen
dc.titleForecasting for Network Management with Joint Statistical Modelling and Machine Learningen
dc.typeconference output*
dc.type.hasVersionAM*
dspace.entity.typePublication
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