Modeling and simulating time series input processes with ARTAFACTS and ARTAGEN

We develop an efficient numerical method for fitting ARTA processes for use as simulation input. ARTA processes are stationary time series with arbitrary marginal distributions and autocorrelations specified through finite lag p. We discuss the software package ARTAFACTS, which implements the numerical method, and the package ARTAGEN, which generates observations from ARTA processes. To demonstrate the use of the software, we present a real-world exaolple.

Modeling and Generating Random Vectors with Arbitrary Marginal Distributions and Correlation Matrix

We describe a model for representing random vectors whose component random variables have arbitrary marginal distributions and correlation matrix, and describe how to generate data based upon this model for use in a stochastic simulation. The central idea is to transform a multivariate normal random vector into the desired random vector, so we refer to these vectors as having a NORTA (NORmal To Anything) distribution. NORTA vectors are most useful when the marginal distributions of the component random variables are neither identical nor from the same family of distributions, and they are particularly valuable when the dimension of the random vector is greater than two. Several numerical examples are provided. Keywords: simulation, random vector, input modeling, correlation matrix, copulas 1 Introduction In many stochastic simulations, simple input models---idependent and identically distributed sequences from standard probability distributions---are not faithful representations of th..

Numerical Methods for Fitting and Simulating Autoregressive-To-Anything Processes

An ARTA (AutoRegressive to Anything) Process is a time series with arbitrary marginal distribution and autocorrelation structure specified through finite lag p. We develop an efficient numerical method for fitting ARTA processes and discuss its implementation in the software ARTAFACTS. We also present the software ARTAGEN that generates observations from ARTA processes for use as inputs to a computer simulation. We illustrate the use of the software with a real-world example. Subject classification: simulation Other Keywords: time series, input modeling, numerical integration Dependent, time-series input processes occur naturally in the simulation of many service, communications and manufacturing systems. For example, the sizes of the demands on an inventory system in successive periods are often dependent because a large demand in one period implies that fewer items will be needed in the following period. Ware, Page and 1 Nelson [13] observed that the times between file accesses on ..

An Investigation Of The Relationship Between Problem Characteristics And Algorithm Performance: A Case Study Of The Gap

We compare synthetic Generalized Assignment Problems (GAP) generated under two correlation-induction strategics: Implicit Correlation Induction (ICI) and Explicit Correlation Induction (ECI). We present computational results for two commercially-available solvers and four heuristics on 590 test problems. We conclude that the solvers’ performances degrade as the population correlation between the objective function and capacity constraint coefficients decreases. However, the heuristics’ performances improved as the absolute value of the same population correlation increases. We find that problems generated under ECI arc more challenging than problems generated under ICI and may lead to a better understanding of the capabilities and limitations of the solution methods being evaluated. We recommend that one consider the purpose(s) of an experiment and types of solution procedures to be evaluated when determining what types of test problems to generate. © 2002, Taylor & Francis Group, LLC

An Investigation Of The Relationship Between Problem Characteristics And Algorithm Performance: A Case Study Of The Gap

We compare synthetic Generalized Assignment Problems (GAP) generated under two correlation-induction strategies: Implicit Correlation Induction (ICI) and Explicit Correlation Induction (ECI). We present computational results for two commercially-available solvers and four heuristics on 590 test problems. We conclude that the solvers\u27 performances degrade as the population correlation between the objective function and capacity constraint coefficients decreases. However, the heuristics\u27 performances improved as the absolute value of the same population correlation increases. We find that problems generated under ECI are more challenging than problems generated under ICI and may lead to a better understanding of the capabilities and limitations of the solution methods being evaluated. We recommend that one consider the purpose(s) of an experiment and types of solution procedures to be evaluated when determining what types of test problems to generate