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

• Published in: Expert systems with applications Vol. 34; no. 1; pp. 665 - 672
• Main Authors: Balaguer, Emili, Palomares, Alberto, Soria, Emilio, Martín-Guerrero, Jose David
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 2008
• Subjects: Call centers; Neural networks; Time series analysis; Call centers; Time series analysis; Neural networks
• ISSN: 0957-4174; 1873-6793
• 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.