Data-driven simulation of complex multidimensional time series

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.