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

Constantinos S. Hilas, Sotirios K. Goudos, John N. Sahalos

Research output: Contribution to journalArticle

23 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)495-509
Number of pages15
JournalTechnological Forecasting and Social Change
Volume73
Issue number5
DOIs
Publication statusPublished - Jun 2006

Keywords

  • Call data pattern recognition
  • Forecast evaluation
  • Model selection
  • Seasonal adjustment
  • Time series

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