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

机译:基于非线性动力学,ARMA建模和神经网络预测支持中心的服务请求

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摘要

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.
机译:在本文中,我们介绍了使用不同的数学模型来预测支持中心(SC)中的服务需求。服务请求的成功预测可以帮助有效管理用于解决这些问题的人力和技术资源。时间序列的非线性分析表明了非线性建模的便利性。使用经典模型(例如自回归移动平均值(ARMA)模型)对基于时延神经网络(TDNN)的神经模型进行基准测试。模型在所需信号与模型预测的相关系数之间实现了较高的相关值(在样本外集合中获得了0.88至0.97之间的值)。结果表明,这些方法适用于SC的管理。

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