Data-driven simulation of complex multidimensional time series

  • Authors:
  • Lee W. Schruben;Dashi I. Singham

  • Affiliations:
  • University of California, Berkeley, CA;Naval Postgraduate School, Monterey, CA

  • Venue:
  • ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special issue on simulation in complex service systems
  • Year:
  • 2014

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Abstract

This article introduces a new framework for resampling general time series data. The approach, inspired by computer agent flocking algorithms, can be used to generate inputs to complex simulation models or for generating pseudo-replications of expensive simulation outputs. The method has the flexibility to enable replicated sensitivity analysis for trace-driven simulation, which is critical for risk assessment. The article includes two simple implementations to illustrate the approach. These implementations are applied to nonstationary and state-dependent multivariate time series. Examples using emergency department data are presented.