Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Evaluating healthcare systems with insufficient capacity to meet demand
Proceedings of the Winter Simulation Conference
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In this work, we extend the use of time series models to the output analysis of non-stationary discrete event simulations. In particular, we investigate and experimentally evaluate the applicability of ARIMA(p, d, q) models as potential meta-models for simulating queueing systems under critical traffic conditions. We exploit stationarity-inducing transformations, in order to efficiently estimate performance measures of selected responses in the system under study.