Applied & computational complex analysis: power series integration conformal mapping location of zero
Modern statistical, systems, and GPSS simulation: the first course
Modern statistical, systems, and GPSS simulation: the first course
On the self-similar nature of Ethernet traffic
SIGCOMM '93 Conference proceedings on Communications architectures, protocols and applications
On the self-similar nature of Ethernet traffic (extended version)
IEEE/ACM Transactions on Networking (TON)
Wide area traffic: the failure of Poisson modeling
IEEE/ACM Transactions on Networking (TON)
TEStool: a visual interactive environment for modeling autocorrelated time series
Performance Evaluation - Special issue: performance modeling tools
Modeling and simulating time series input processes with ARTAFACTS and ARTAGEN
WSC '96 Proceedings of the 28th conference on Winter simulation
Second order stochastic simulation with specified correlation
Advances in Engineering Software
Time Series Analysis
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Numerical Methods for Fitting and Simulating Autoregressive-To-Anything Processes
INFORMS Journal on Computing
Autoregressive to anything: Time-series input processes for simulation
Operations Research Letters
| Hi-index | 0.00 |
In this paper, we focus on inter-arrival time autocorrelation and its impact on model performance. We present a technique to generate matrix exponential random variables that match first-order statistics (moments) and second-order statistics (autocorrelation) from an empirical distribution. We briefly explain the matrix exponential distribution and show that we can represent any empirical distribution arbitrarily closely as matrix exponential. We then show how we can incorporate an autocorrelation structure into our matrix exponential random variables using the autoregressive to anything technique. We present examples showing how we match first and second-order statistics from empirical distributions and finally we show that our autocorrelation matrix exponential random variables produce more accurate performance metrics from simulation models than traditional techniques.