Modeling and Generating Multivariate Time Series with Arbitrary Marginals and Autocorrelation Structures

Providing accurate and automated input modeling support is one of the challenging problems in the application of computer simulation. In this paper, we present a general-purpose input-modeling tool for representing, fitting, and generating random variates from multivariate input processes to drive computer simulations. We explain the theory underlying the suggested data fitting and data generation techniques, and demonstrate that our framework fits models accurately to both univariate and multivariate input processes.