Forecasting incoming call volumes in call centers with recurrent Neural Networks

Research output: Contribution to journalArticle


Researchers apply Neural Networks widely in model prediction and data mining because of their remarkable approximation ability. This study uses a prediction model based on the Elman and NARX Neural Network and a back-propagation algorithm for forecasting call volumes in call centers. The results can help determine the optimal number of agents necessary to reduce waiting time for customers, enabling profit maximization and reduction of unnecessary costs. This study also compares the performance of the Elman-NARX Neural Network model with the time-lagged feed-forward Neural Network in addressing the same problem. The experimental results indicate that the proposed method is efficient in forecasting the call volumes of call centers.


External organisations
  • K. N. Toosi University of Technology
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Computer Systems


  • Call center, Forecasting, Model prediction, Neural Networks
Original languageEnglish
Pages (from-to)4811-4814
Number of pages4
JournalJournal of Business Research
Issue number11
Publication statusPublished - 2016 Nov
Publication categoryResearch