@article{
 title = {Seasonal decomposition and forecasting of telecommunication data: A comparative case study},
 type = {article},
 year = {2006},
 keywords = {Call data pattern recognition,Forecast evaluation,Model selection,Seasonal adjustment,Time series},
 pages = {495-509},
 volume = {73},
 id = {7fa58d5e-5622-36fd-b989-a5965df6cd25},
 created = {2020-02-29T16:57:43.049Z},
 file_attached = {false},
 profile_id = {c69aa657-d754-373c-91b7-64154b7d5d91},
 last_modified = {2023-02-11T18:08:48.917Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Hilas2006},
 private_publication = {false},
 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. © 2005 Elsevier Inc. All rights reserved.},
 bibtype = {article},
 author = {Hilas, Constantinos S. and Goudos, Sotirios K. and Sahalos, John N.},
 doi = {10.1016/j.techfore.2005.07.002},
 journal = {Technological Forecasting and Social Change},
 number = {5}
}

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