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

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.

RIV: BC