A guide to simulation (2nd ed.)
A guide to simulation (2nd ed.)
TEStool: a visual interactive environment for modeling autocorrelated time series
Performance Evaluation - Special issue: performance modeling tools
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Bidirectional Estimation and Confidence Regions for TES Processes
MASCOTS '95 Proceedings of the 3rd International Workshop on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems
An Overview of Tes Processes and Modeling Methodology
Performance Evaluation of Computer and Communication Systems, Joint Tutorial Papers of Performance '93 and Sigmetrics '93
Automated TES modeling of compressed video
INFOCOM '95 Proceedings of the Fourteenth Annual Joint Conference of the IEEE Computer and Communication Societies (Vol. 2)-Volume - Volume 2
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Forecasting is of prime importance for accuracy in decision making. For data sets containing high autocorrelations, failure to account for temporal dependence will result in poor forecasting. TES (Transform-Expand-Sample) is a class of stochastic processes to model empirical autocorrelated time series and is used in Monte Carlo simulation. Its merit is to simultaneously capture both the empirical distribution function and the autocorrelation function. The transition structure of TES processes can be utilized to calculate forecasts for future periods. In this paper, we utilize phase-type random variables as the innovation density in TES model fitting methodology, and we investigate the forecasting performance of TES processes compared to traditional auto regressive integrated moving-average models. We find that TES models yield forecasts as accurate as time series models.