Online Prediction under Model Uncertainty Via Dynamic Model Averaging: Application to a Cold Rolling Mill

0342595 - ÚTIA 2011 RIV US eng J - Journal Article
Raftery, A. E. - Kárný, Miroslav - Ettler, P.
Online Prediction under Model Uncertainty Via Dynamic Model Averaging: Application to a Cold Rolling Mill.
Technometrics. Volume 52, Number 1 (2010), s. 52-66. ISSN 0040-1706. E-ISSN 1537-2723
R&D Projects: GA MŠMT 1M0572; GA MŠMT(CZ) 7D09008
Institutional research plan: CEZ:AV0Z10750506
Keywords : prediction * rolling mills * Bayesian Dynamic Averaging
Subject RIV: BC - Control Systems Theory
Impact factor: 1.560, year: 2010
http://library.utia.cas.cz/separaty/2010/AS/karny-0342595.pdf

We consider the problem of online prediction when it is uncertain what the best prediction model to use is. We develop a method called Dynamic Model Averaging (DMA) in which a state space model for the parameters of each model is combined with a Markov chain model for the correct model. This allows the "correct" model to vary over time. The state space and Markov chain models are both specied in terms of forgetting, leading to a highly parsimonious representation. As a special case, when the model and parameters do not change, DMA is a recursive implementation of standard Bayesian model averaging, which we call recursive model averaging (RMA). The method is applied to the problem of predicting the output strip thickness for a cold rolling mill, where the output is measured with a time delay.
Permanent Link: http://hdl.handle.net/11104/0185291