Fitting time-series input processes for simulation

Bahar Biller*, Barry L. Nelson

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

35 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)549-559
Number of pages11
JournalOperations Research
Issue number3
StatePublished - May 2005


  • Correlation
  • Estimation
  • Least-squares fitting
  • Simulation
  • Statistical analysis: stochastic input modeling
  • Statistics
  • Time series: autoregressive processes

ASJC Scopus subject areas

  • Computer Science Applications
  • Management Science and Operations Research


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