Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
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)
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Performance Evaluation of Complex Systems: Techniques and Tools, Performance 2002, Tutorial Lectures
Fitting world-wide web request traces with the EM-algorithm
Performance Evaluation - Special issue: Internet performance and control of network systems
Proceedings of the 35th conference on Winter simulation: driving innovation
Fitting Time-Series Input Processes for Simulation
Operations Research
A Novel Approach for Phase-Type Fitting with the EM Algorithm
IEEE Transactions on Dependable and Secure Computing
An overview of the OMNeT++ simulation environment
Proceedings of the 1st international conference on Simulation tools and techniques for communications, networks and systems & workshops
A Heuristic Approach for Fitting MAPs to Moments and Joint Moments
QEST '09 Proceedings of the 2009 Sixth International Conference on the Quantitative Evaluation of Systems
Correlated phase-type distributed random numbers as input models for simulations
Performance Evaluation
Simulating stochastic processes with OMNeT++
Proceedings of the 4th International ICST Conference on Simulation Tools and Techniques
Autoregressive to anything: Time-series input processes for simulation
Operations Research Letters
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The adequate modeling of correlated input processes is necessary to obtain realistic models in areas like computer or communication networks but is still a challenge in simulation modeling. In this paper we present a new class of stochastic processes which has been developed for describing correlated input processes and combines acyclic Phase-type distributions to model the marginal distribution with an ARMA process to capture the autocorrelation. The processes are an extension of ARTA processes, a well established input model in stochastic simulations. For the new process type we propose a fitting algorithm that allows one to approximate arbitrary sets of joint moments and autocorrelation coefficients and investigate the effect of different sets of approximated quantities on the quality of the fitted process empirically. We furthermore present an efficient way to generate random numbers and show how the processes can be easily integrated into simulation models.