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.
Original language | English (US) |
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Pages (from-to) | 549-559 |
Number of pages | 11 |
Journal | Operations Research |
Volume | 53 |
Issue number | 3 |
DOIs | |
State | Published - May 2005 |
Keywords
- 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