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Forecasting incoming call volumes in call centers with recurrent Neural Networks

Ebadi Jalal, Mona ; Hosseini, Monireh and Karlsson, Stefan LU (2016) In Journal of Business Research 69(11). p.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.

Please use this url to cite or link to this publication:
author
; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Call center, Forecasting, Model prediction, Neural Networks
in
Journal of Business Research
volume
69
issue
11
pages
4 pages
publisher
Elsevier
external identifiers
  • scopus:84966565268
  • wos:000383936800015
ISSN
0148-2963
DOI
10.1016/j.jbusres.2016.04.035
language
English
LU publication?
yes
id
6ea02896-08e1-477a-a97c-58a225e49a96
date added to LUP
2016-10-05 09:54:51
date last changed
2025-02-08 15:29:50
@article{6ea02896-08e1-477a-a97c-58a225e49a96,
  abstract     = {{<p>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.</p>}},
  author       = {{Ebadi Jalal, Mona and Hosseini, Monireh and Karlsson, Stefan}},
  issn         = {{0148-2963}},
  keywords     = {{Call center; Forecasting; Model prediction; Neural Networks}},
  language     = {{eng}},
  number       = {{11}},
  pages        = {{4811--4814}},
  publisher    = {{Elsevier}},
  series       = {{Journal of Business Research}},
  title        = {{Forecasting incoming call volumes in call centers with recurrent Neural Networks}},
  url          = {{http://dx.doi.org/10.1016/j.jbusres.2016.04.035}},
  doi          = {{10.1016/j.jbusres.2016.04.035}},
  volume       = {{69}},
  year         = {{2016}},
}