A Survey of TES Modeling Applications

Abstract

Princeton, NJ 08540 TES (Transform-Expand-Sample) is a versatile methodology for modeling general stationary time series, and particularly those that are autocorrelated. From the viewpoint of Monte Carlo simulation, TES represents a new and flexible input analysis approach. The salient feature of TES is its potential ability to simultaneously capture first-order and second-order properties of empirical time series (field measurements): Given an empirical sample, TES is designed to fit an arbitrary empirical marginal distribution (histogram) and to simultaneously approximate the leading empirical autocorrelations. Practical TES modeling is computationally intensive and can be effectively carried out only with computer support. A software modeling environment, called TEStool, has been designed and implemented to support the TES modeling methodology, through an interactive heuristic or algorithmic search approach employing extensive visualization. The purpose of this paper is to introduce TES modeling, and to offer some illustrative examples from a range of applications, including source modeling of compressed video and fault arrivals, financial modeling and texture generation. These examples demonstrate the efficacy and versatility of the TES modeling methodology, and underscore the high fidelity attainable with TES models.

Autocorrelation effects in manufacturing systems performance: A simulation analysis

“…They later proposed an approximate method to analyze the effect of splitting and superposition of autocorrelation processes in queues (Balcıoglu et al, 2008). Some works have shown examples of autocorrelation in data from industrial plants (Luxhoj and Shyur, 1995;Melamed and Hill, 1995;Mertens et al 2009;Schomig and Mittler, 1995;Young and Winistorfer, 2001).…”

Low bit-rate video transmission over wireless Zigbee networks

“…In [17], an investigation of TES modeling and multiplexing of MPEG traffic over ATM networks was performed. In [18] [19] H.261 video compression is modeled using the TES methodology. In [20], TES modeling is used to generate models for high bit-rate MPEG4 traffic.…”

“…In temporal redundancy, only the differences between successive frames are encoded using frame differencing, motion estimation and motion-compensated prediction [25]. Since the overall video sequence is not stationary as can be seen from the previously mentioned figures, and because of the periodic nature of the autocorrelation function, a TES + model is applied for the I and B frame statistics and a TES~ model is applied for the P frame statistics as it was performed in [18]. In other words, only one model of the video is required, while the rest can just be multiplied by a scaling factor that depends on the video type.…”

Simulation platform for the planning and design of networks carrying VoIP traffic

“…Analyzing real-world random phenomena using exclusively first-order statistic, is in many cases a valid approach; in other instances, it is a common oversimplification mistake. For variable data rate audio and video algorithms, second-order statistic dependence is to be expected [80]. Undermodeling is the term used in [81] referring to the case where some of dependencies that are relevant to the system in consideration are overlooked.…”