A comprehensive input-modeling framework and software for stochastic, discrete-event simulation experiments

Providing accurate and automated input modeling support is one of the challenging problems in the application of computer simulation. The models incorporated in current input-modeling software packages often fall short of what is needed because they emphasize independent and identically distributed processes, while dependent multivariate time-series processes occur naturally in the simulation of many real-life systems. We present a model for representing stationary multivariate time-series input processes with marginal distributions from the Johnson translation system and an autocorrelation structure specified through some finite lag. We then describe how to generate data accurately to drive computer simulations. We also introduce a statistical methodology for fitting stochastic models to dependent time-series input processes. Specifically, an automated and statistically valid algorithm is presented to fit ARTA (Autoregressive-to-Anything) processes with marginal distributions from the Johnson translation system to stationary univariate time-series data. We illustrate the use of the data-generation and data-fitting procedures via examples and provide empirical comparisons with some existing input-modeling procedures.