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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 ana 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.