Abstract: TES (Transform-Expand-Sample) is a versatile class of stationary stochastic processes which can model arbitrary marginals, a wide variety of autocorrelation functions, and a broad range of sample path behaviors. An algorithmic method replaces the previously relied upon heuristic search, thereby automating TES modeling for simulation analysis. The algorithm is solved in a nonlinear programming setting, which takes advantage of fast and accurate computation of TES autocorrelation functions and their partial derivatives to implement a steepest-descent technique, preferably based on Zoutendijk's Feasible Direction Method. The method has particular application to data compression and specifically compressed video.

Abstract: To provide a time series having correlated interarrival times for use in traffic and queueing studies, an independent identically distributed random number stream is first transformed to a sequence of correlated uniform variates by a special modulo-1 autoregressive operation and then further transformed into a sequence of correlated variates with a prescribed marginal distribution from which there is developed the desired time studies.