Input modeling and its impact: modeling and generating multivariate time series with arbitrary marginals and autocorrelation structures

ABSTRACT

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.

REFERENCES

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.1 Logical Design
          Subjects: Schema and subschema

Additional Classification:
  G. Mathematics of Computing
  G.3 PROBABILITY AND STATISTICS
      Subjects: Correlation and regression analysis

  I. Computing Methodologies
  I.6 SIMULATION AND MODELING
      I.6.1 Simulation Theory
          Subjects: Model classification

General Terms:
Algorithms, Design, Performance