Copula-Based Multivariate Input Models for Stochastic Simulation
Operations Research
Generating random AR(p) and MA(q) Toeplitz correlation matrices
Journal of Multivariate Analysis
Simulating cointegrated time series
Winter Simulation Conference
Winter Simulation Conference
Correlated phase-type distributed random numbers as input models for simulations
Performance Evaluation
Simulating stochastic processes with OMNeT++
Proceedings of the 4th International ICST Conference on Simulation Tools and Techniques
ProFiDo – a toolkit for fitting input models
MMB&DFT'10 Proceedings of the 15th international GI/ITG conference on Measurement, Modelling, and Evaluation of Computing Systems and Dependability and Fault Tolerance
A Copulas-Based Approach to Modeling Dependence in Decision Trees
Operations Research
Traffic modeling with a combination of phase-type distributions and ARMA processes
Proceedings of the Winter Simulation Conference
Strength of tail dependence based on conditional tail expectation
Journal of Multivariate Analysis
| Hi-index | 0.00 |
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.