Comparing deep and shallow neural networks in forecasting call center arrivals

Abstract

Forecasting volumes of incoming calls is the first step of the workforce planning process in call centers and represents a prominent issue from both research and industry perspectives. We investigate the application of Neural Networks to predict incoming calls 24 hours ahead. In particular, a Machine Learning deep architecture known as Echo State Network, is compared with a completely different rolling horizon shallow Neural Network strategy, in which the lack of recurrent connections is compensated by a careful input selection. The comparison, carried out on three different real world datasets, reveals better predictive performance for the shallow approach. The latter appears also more robust and less demanding, reducing the inference time by a factor of 2.5 to 4.5 compared to Echo State Networks.