Creation of sufficiently accurate workload models of computer systems is a key step in evaluating and tuning these systems. Workload models for an observable system can be built from traces collected by observing the system. This dissertation presents a novel technique to construct non-executable, artificial workload models fitting transient trace data. The trace can be a categorical or numerical time-series. The trace is considered a sample realization of a non-stationary stochastic process, {X_t}, such that random variables X_t follow different probability distributions. To estimate the parameters for the model a Rate Evolution Graph (REG) is built from the trace data. The REG is a two-dimensional Cartesian graph which plots the number of occurrences of each unique state in the trace on the ordinate and time on the abscissa. The REG contains one path for all instances of each unique state in the trace. The derivative of a REG path at time t is used as an estimate of the probability of occurrence of the corresponding state at t. We use piecewise linear regression to fit straight line segments to each REG path. The slopes of the line segments that fit a REG path estimate the time dependent probability of occurrence of the corresponding state. The estimates of occurrence probabilities of all unique states in the trace are used to construct a time-dependent joint probability mass function. The joint probability mass function is the representation of the Pzrecewise Independent Stochastic Process model for the trace. Two methods that assist to compact the model, while retaining accuracy, are also discussed. / Ph. D.