The empirical TES methodology: modeling empirical time series. (1997)

• Record Type: Journal Article
• Authors: Melamed, Benjamin
• Is Part Of: Journal of applied mathematics and stochastic analysis. Volume 10:Number 4(1997)
• Page Start: 333
• Page End: 353
• Publication Date: 1997
• Subjects: autocorrelation functions -- histograms -- modeling time series -- model fitting -- TES processes -- TES sequences; Mathematical models -- Periodicals; Computer simulation -- Periodicals; Computer science -- Mathematics -- Periodicals; Computer science -- Mathematics; Computer simulation; Mathematical models; Applied Mathematics; Periodicals; Electronic journals; 519.22
• DOI: 10.1155/S1048953397000403
• Languages: English
• ISSNs: 1048-9533

Abstract

Abstract : TES (Transform-Expand-Sample) is a versatile class of stochastic sequences defined via an autoregressive scheme with modulo-1 reduction and additional transformations. The scope of TES encompasses a wide variety of sample path behaviors, which in turn give rise to autocorrelation functions with diverse functional forms - monotone, oscillatory, alternating, and others. TES sequences are readily generated on a computer, and their autocorrelation functions can be numerically computed from accurate analytical formulas at a modest computational cost. This paper presents the empirical TES modeling methodology which uses TES process theory to model empirical records. The novel feature of the TES methodology is that it expressly aims to simultaneously capture the empirical marginal distribution (histogram) and autocorrelation function. We draw attention to the non-parametric nature of TES modeling in that it always guarantees an exact match to the empirical marginal distribution. However, fitting the corresponding autocorrelation function calls for a heuristic search for a TES model over a large parametric space. Consequently, practical TES modeling of empirical records must currently rely on software assistance. A visual interactive software environment, called TEStool, has been designed and implemented to support TES modeling. The paper describes the empirical TES modeling methodology as implemented in TEStool and provides numerically-computable formulas for TES autocorrelations. Two examples illustrate the efficacy of the TES modeling approach. These examples serve to highlight the ability of TES models to capture first-order and second-order properties of empirical sample paths and to mimic their qualitative appearance.