Modeling and Generating Multivariate Time-Series Input Processes Using a Vector Autoregressive Technique

Bahar Biller*, Barry L. Nelson

*Corresponding author for this work

Research output: Contribution to journalArticle

69 Scopus citations

Abstract

We present a model for representing stationary multivariate time-series input processes with marginal distributions from the Johnson translation system and an autocorrelation structure specified through some finite lag. We then describe how to generate data accurately to drive computer simulations. The central idea is to transform a Gaussian vector autoregressive process into the desired multivariate time-series input process that we presume as having a VARTA (Vector-Autoregressive-To-Anything) distribution. We manipulate the autocorrelation structure of the Gaussian vector autoregressive process so that we achieve the desired autocorrelation structure for the simulation input process. We call this the correlation-matching problem and solve it by an algorithm that incorporates a numerical-search procedure and a numerical-integration technique. An illustrative example is included.

Original languageEnglish (US)
Pages (from-to)211-237
Number of pages27
JournalACM Transactions on Modeling and Computer Simulation
Volume13
Issue number3
DOIs
StatePublished - Jul 2003

Keywords

  • Input modeling
  • Multivariate time series
  • Numerical integration
  • Vector autoregressive process

ASJC Scopus subject areas

  • Modeling and Simulation
  • Computer Science Applications

Fingerprint Dive into the research topics of 'Modeling and Generating Multivariate Time-Series Input Processes Using a Vector Autoregressive Technique'. Together they form a unique fingerprint.

  • Cite this