Parallelization algorithms for modeling ARM processes
نویسندگان
چکیده
منابع مشابه
Paralleijzation Algorithms for Modeling Arm Processes
AutoRegressive Modular (ARM) processes are a new class of nonlinear stochastic processes, which can accurately model a large class of stochastic processes, by capturing the empirical distribution and autocorrelation function simultaneously. Given an empirical sample path, the ARM modeling procedure consists of two steps: a global search for locating the minima of a nonlinear objective function ...
متن کاملArm Processes and Modeling Methodology
ARM (Auto-Regressive Modular) processes constitute a broad class of nonlinear autoregressive schemes with modulo-1 reduction and additional transformations. Unlike their TES (Transform-Expand-Sample) precursors, which only admit iid innovation sequences, ARM processes admit dependent innovation sequences as well, so long as they are independent of the initial ARM variate. As such, the class of ...
متن کاملARM Processes and Their Modeling and ForecastingMethodology
The class of ARM (Autoregressive Modular) processes is a class of stochastic processes, defined by a nonlinear autoregressive scheme with modulo-1 reduction and additional transformations. ARM processes constitute a versatile class designed to produce high-fidelity models from stationary empirical time series by fitting a strong statistical signature consisting of the empirical marginal distrib...
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In this paper we investigate the parallelization of two modular algorithms. In fact, we consider the modular computation of Gröbner bases (resp. standard bases) and the modular computation of the associated primes of a zero–dimensional ideal and describe their parallel implementation in Singular. Our modular algorithms to solve problems over Q mainly consist of three parts, solving the problem ...
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This chapter is devoted to a comparative survey of loop parallelization algorithms. Various algorithms have been presented in the literature, such as those introduced These algorithms make use of diierent mathematical tools. Also, they do not rely on the same representation of data dependences. In this chapter, we survey each of these algorithms, and we assess their power and limitations, both ...
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ژورنال
عنوان ژورنال: Journal of Applied Mathematics and Stochastic Analysis
سال: 2000
ISSN: 1048-9533,1687-2177
DOI: 10.1155/s1048953300000332