Seasonal decomposition and forecasting of telecommunication data: A comparative case study

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dc.contributor.author Hilas, Constantinos S.
dc.contributor.author Goudos, Sotirios K.
dc.contributor.author Sahalos, John N.
dc.date.accessioned 2015-06-25T09:50:25Z
dc.date.available 2015-06-25T09:50:25Z
dc.date.issued 2006-06
dc.identifier.other http://www.sciencedirect.com/science/article/pii/S0040162505001101 el
dc.identifier.uri http://apothesis.teicm.gr/xmlui/handle/123456789/1476
dc.description.abstract In this paper, forecasting models for the monthly outgoing telephone calls in a University Campus are presented. The data have been separated in the categories of international and national calls as well as calls to mobile phones. The total number of calls has also been analyzed. Three different methods, namely the Seasonal Decomposition, Exponential Smoothing Method and SARIMA Method, have been used. Forecasts with 95% confidence intervals were calculated for each method and compared with the actual data. The outcome of this work can be used to predict future demands for the telecommunications network of the University. en
dc.format.extent 15 el
dc.language.iso en el
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 Διεθνές *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/ *
dc.title Seasonal decomposition and forecasting of telecommunication data: A comparative case study en
dc.type Άρθρο σε επιστημονικό περιοδικό el
dc.identifier.doi 10.1016/j.techfore.2005.07.002
dc.publication.category Απαγόρευση δημοσίευσης - Βιβλιογραφική αναφορά el
dc.relation.journal Technological Forecasting and Social Change;Vol. 73, Iss. 5
dc.subject.keyword Forecast evaluation el
dc.subject.keyword Seasonal adjustment el
dc.subject.keyword Time series el
dc.subject.keyword Call data pattern recognition el
dc.subject.keyword Model selection el


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Attribution-NonCommercial-NoDerivatives 4.0 Διεθνές Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Διεθνές