Fitting Time-Series Input Processes for Simulation

Bahar Biller () and Barry L. Nelson ()
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Bahar Biller: Tepper School of Business, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
Barry L. Nelson: Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, Illinois 60208

Operations Research, 2005, vol. 53, issue 3, 549-559

Abstract: Providing accurate and automated input-modeling support is one of the challenging problems in the application of computer simulation of stochastic systems. The models incorporated in current input-modeling software packages often fall short because they assume independent and identically distributed processes, even though dependent time-series input processes occur naturally in the simulation of many real-life systems. Therefore, 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 autoregressive-to-anything (ARTA) processes with marginal distributions from the Johnson translation system to stationary univariate time-series data. ARTA processes are particularly well suited to driving stochastic simulations. The use of this algorithm is illustrated with examples.

Keywords: simulation; statistical analysis:stochastic input modeling; statistics; correlation; estimation; time series:autoregressive processes; least-squares fitting (search for similar items in EconPapers)
Date: 2005
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Citations: View citations in EconPapers (11)

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