Parameter estimation for ARTA processes

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 time-series processes occur naturally in the simulation of many real-life systems. This paper introduces 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. The use of this algorithm is illustrated via a real-life example.