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 Deep Learning method known as Echo State Networks, is compared with a completely different shallow Neural Networks 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 similar predictive performance, although the shallow approach seems to be more robust and less demanding in terms of time-to-predict.