  Motivated by interest in providing more efficient services in customer
service systems, we use statistical learning methods and delay history
information to predict the conditional distribution of the customers' waiting
times in queueing systems. From the predicted distributions, descriptive
statistics of the system such as the mean, variance and percentiles of the
waiting times can be obtained, which can be used for delay announcements, SLA
conformance and better system management. We model the conditional
distributions by mixtures of Gaussians, parameters of which can be estimated
using Mixture Density Networks. The evaluations show that exploiting more delay
history information can result in much more accurate predictions under
realistic time-varying arrival assumptions.
