Multivariate statistical simulation
Multivariate statistical simulation
Kendall's advanced theory of statistics
Kendall's advanced theory of statistics
The impact of autocorrelation on queuing systems
Management Science
The TES methodology: modeling empirical stationary time series
WSC '92 Proceedings of the 24th conference on Winter simulation
Automatic modeling of file system workloads using two-level arrival processes
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Numerical Methods for Fitting and Simulating Autoregressive-To-Anything Processes
INFORMS Journal on Computing
Initialization for NORTA: Generation of Random Vectors with Specified Marginals and Correlations
INFORMS Journal on Computing
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Autoregressive to anything: Time-series input processes for simulation
Operations Research Letters
ACM Transactions on Modeling and Computer Simulation (TOMACS)
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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 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.