  Call centers' managers are interested in obtaining accurate point and
distributional forecasts of call arrivals in order to achieve an optimal
balance between service quality and operating costs. We present a strategy for
selecting forecast models of call arrivals which is based on three pillars: (i)
flexibility of the loss function; (ii) statistical evaluation of forecast
accuracy; (iii) economic evaluation of forecast performance using money
metrics. We implement fourteen time series models and seven forecast
combination schemes on three series of daily call arrivals. Although we focus
mainly on point forecasts, we also analyze density forecast evaluation. We show
that second moments modeling is important both for point and density
forecasting and that the simple Seasonal Random Walk model is always
outperformed by more general specifications. Our results suggest that call
center managers should invest in the use of forecast models which describe both
first and second moments of call arrivals.
