  This paper develops a generalization of Brownian motion with stationary,
autocorrelated increments as a tractable model for problems in business and
finance. We show that any real continuous Gaussian Markov process with
stationary increments and smooth covariance function is characterized by three
parameters quantifying drift, volatility, and autocorrelations. We model a
queue as a functional of a process defined by those characteristics and derive
its transient distribution conditional on its history.
