Predicting service request in support centers based on nonlinear dynamics, ARMA modeling and neural networks

This source preferred by Emili Balaguer-Ballester

Authors: Balaguer, E., Palomares, A., Soria, E. and Martín-Guerrero, J.D.

Journal: Expert Systems with Applications

Volume: 34

Pages: 665-672

Publisher: Elsevier

This data was imported from DBLP:

Authors: Balaguer, E., Palomares, A., Soria-Olivas, E. and Martín-Guerrero, J.D.

Journal: Expert Syst. Appl.

Volume: 34

Pages: 665-672

This data was imported from Scopus:

Authors: Balaguer, E., Palomares, A., Soria, E. and Martín-Guerrero, J.D.

Journal: Expert Systems with Applications

Volume: 34

Issue: 1

Pages: 665-672

ISSN: 0957-4174

DOI: 10.1016/j.eswa.2006.10.003

In this paper, we present the use of different mathematical models to forecast service requests in support centers (SCs). A successful prediction of service request can help in the efficient management of both human and technological resources that are used to solve these eventualities. A nonlinear analysis of the time series indicates the convenience of nonlinear modeling. Neural models based on the time delay neural network (TDNN) are benchmarked with classical models, such as auto-regressive moving average (ARMA) models. Models achieved high values for the correlation coefficient between the desired signal and that predicted by the models (values between 0.88 and 0.97 were obtained in the out-of-sample set). Results show the suitability of these approaches for the management of SCs. © 2006 Elsevier Ltd. All rights reserved.

This data was imported from Web of Science (Lite):

Authors: Balaguer, E., Palomares, A., Soria, E. and Martin-Guerrero, J.D.

Journal: EXPERT SYSTEMS WITH APPLICATIONS

Volume: 34

Issue: 1

Pages: 665-672

ISSN: 0957-4174

DOI: 10.1016/j.eswa.2006.10.003

The data on this page was last updated at 05:12 on February 26, 2020.