TEStool: A visual interactive environment for modeling autocorrelated time series

Hill, Jon R.; Melamed, Benjamin

doi:10.1016/0166-5316(95)00006-j

Cited by 19 publications

(7 citation statements)

References 25 publications

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“…Figure 7 displays a screen image of TEStool (a software package supporting TES modeling (Hill and Melamed 1995) Observe that all statistics exhibit excellent agreement between the empirical data and their simulationbased counterparts.…”

Section: Model Validationmentioning

confidence: 94%

Modeling compressed full-motion video

Melamed

1997

Proceedings of the 29th Conference on Winter Simulation - WSC '97

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This paper presents a general approach to modeling VBR (variable bit rate) compressed full-motion video. The salient feature of such video is the existence of scenes. Scene structure presents special problems if one is to construct high-fidelity source models to drive Monte Carlo simulations.Intuitively, scene transitions are often accompanied by a substantial change of bit rate (the size of compressed pictures), as well as the level of burstiness. This paper argues that a modulated model can handle well scene modeling, and illustrates this point by an example.

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Section: Model Validationmentioning

confidence: 94%

Modeling compressed full-motion video

Melamed

1997

Proceedings of the 29th Conference on Winter Simulation - WSC '97

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“…TEStool is a visual interactive software environment designed for fitting TES models to empirical data [7,27,9]. It provides services to construct and modify TES models in the heuristic search mode, to generate simulated sample paths and to examine their statistics, as well as to launch algorithmic searches.…”

Section: The Testool Modeling Envirorunentmentioning

confidence: 99%

“…Consequently, TES modeling of empirical data may be carried out interactively and visually. This observation motivated the design and implementation of a TES-based modeling package, called TEStool, which makes heavy use of visualization in order to provide a pleasant interactive environment for modeling dependent stationary time series [7,9]. Two search approaches have been implemented: heuristic and algorithmic.…”

Section: Introductionmentioning

confidence: 99%

A Survey of TES Modeling Applications

Melamed¹,

Hill²

1995

SIMULATION

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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.

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“…It is a general method in that it can match any marginal distribution exactly and simultaneously approximate a wide variety of autocorrelation functions. A software package called TEStool supports T ES modeling via a graphical user interface, which facilitates both algorithms and interactive searches for TES models [23]. Recently, Jelenkovic and Melamed [24] have developed an algorithm which largely automates the search process and invariably leads to a more accurate model than those obtained via human interaction.…”

Section: Models Based On the Tes Processmentioning

confidence: 99%

A survey of statistical source models for variable-bit-rate compressed video

Izquierdo¹,

Reeves

1999

Multimedia Systems

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Abstract. It is predicted that, in the near future, the transport of compressed video will pervade computer networks. Variable-bit-rate (VBR) encoded video is expected to become a significant source of network traffic, due to its advantages in statistical multiplexing gain and consistent video quality. Both systems analysts and developers need to assess and study the impact these sources will have on their networks and networking products. To this end, suitable statistical source models are required to analyze performance metrics such as packet loss, delay and jitter. This paper provides a survey of VBR source models which can be used to drive network simulations. The models are categorized into four groups: Markov chain/linear regression, TES, self-similar and i.i.d/analytical. We present models which have been used for VBR sources containing moderate-to-significant scene changes and moderate-to-full motion. A description of each model is given along with corresponding advantages and shortcomings. Comparisons are made based on the complexity of each model.

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TEStool: A visual interactive environment for modeling autocorrelated time series

Cited by 19 publications

References 25 publications

Modeling compressed full-motion video

Modeling compressed full-motion video

A Survey of TES Modeling Applications

A survey of statistical source models for variable-bit-rate compressed video

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