Institute of Information Theory and Automation

You are here

Publication details

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

Journal Article

Raftery A. E., Kárný Miroslav, Ettler P.

serial: Technometrics, p. 52-66

research: CEZ:AV0Z10750506

project(s): 1M0572, GA MŠk, 7D09008, GA MŠk

keywords: prediction, rolling mills, Bayesian Dynamic Averaging

preview: Download

abstract (eng):

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.


bocek: 2012-12-21 16:10