6533b7d4fe1ef96bd126310c

RESEARCH PRODUCT

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

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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.