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Multiple Model Adaptive Estimation For Time Series Analysis, Ibrahim Dulger
Multiple Model Adaptive Estimation For Time Series Analysis, Ibrahim Dulger
Theses and Dissertations
Multiple Model Adaptive Estimation (MMAE) is a Bayesian technique that applies a bank of Kalman filters to predict future observations. Each Kalman filter is based on a different set of parameters and hence produces different residuals. The likelihood of each Kalman filter's prediction is determined by a magnitude of the residuals. Since some researchers have obtained good forecasts using a single Kalman filter, we tested MMAE's ability to make time series predictions. Our Kalman filters have a dynamics model based on a Box-Jenkins Auto-Regressive Moving Average (ARMA) model and a measure model with additive noise. The time-series prediction is based …
Identification Of The Initial Transient In Discrete-Event Simulation Output Using The Kalman Filter, Mark A. Gallagher
Identification Of The Initial Transient In Discrete-Event Simulation Output Using The Kalman Filter, Mark A. Gallagher
Theses and Dissertations
Data truncation is a commonly accepted method of dealing with initialization bias in discrete-event simulation. Algorithms for determining the appropriate initial-data truncation point for univariate and multivariate output are proposed. The techniques entail averaging across replications and estimating a steady-state output model in a state-space framework. Using the estimated model, Multiple Model Adaptive Estimation (MMAE), which uses Kalman filters with different parameter vectors, is applied. Based on the filters' residuals, the conditional probabilities of each filter's specific parameter vector being correct are determined. The MMAE parameter estimates are the probabilistic- weighted average of the filters' assumed parameter vectors. The estimated …