Forecasting incoming call volumes in call centers with recurrent Neural Networks

Mona Ebadi Jalal, Monireh Hosseini and Stefan Karlsson

Journal of Business Research, 2016, vol. 69, issue 11, 4811-4814

Abstract: 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.

Keywords: Forecasting; Model prediction; Call center; Neural Networks (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jbrese:v:69:y:2016:i:11:p:4811-4814

DOI: 10.1016/j.jbusres.2016.04.035

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