A guide to simulation (2nd ed.)
A guide to simulation (2nd ed.)
The Art of Computer Programming, 2nd Ed. (Addison-Wesley Series in Computer Science and Information
The Art of Computer Programming, 2nd Ed. (Addison-Wesley Series in Computer Science and Information
The TES Methodology: Modeling Temporal Dependence in Empirical Time Series
MASCOTS '93 Proceedings of the International Workshop on Modeling, Analysis, and Simulation On 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
An experimental study on forecasting using TES processes
WSC '04 Proceedings of the 36th conference on Winter simulation
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TES (Transform-Expand-Sample) is a versatile class of stationary stochastic processes which can model arbitrary marginals, a wide variety of autocorrelation functions, and a broad range of sample path behaviors. The TES modeling methodology aims to simultaneously capture the empirical marginal distribution (histogram) and autocorrelation function of empirical time series, assuming only that they are from a stationary probability law. In this paper we utilize the known transition structure of TES processes to calculate bidirectional point estimates for these processes as conditional expectations of the process and its time-reversed version, given the current value. We also show how to construct symmetric confidence regions about these point estimates. We demonstrate our results with an example, using the software environment, TEStool, which supports TES modeling.