Abstract
Autoregressive models with exogenous inputs are useful tools for analyzing systems with unknown dynamics, but are limited by the assumption that the relations between inputs and output(s) are linear. For complex systems with nonlinear or abruptly changing dynamics it is possible to modify the technique by allowing for multiple local models and designing a strategy for switching between them. A method by which this can be realized is developed in the paper. The technique is applied on a complex problem in the metallurgical industry, i.e., the prediction of hot metal silicon content in the blast furnace. A set of local models is developed for different parts of a training set, using a statistical criterion for model selection. The resulting local models are then applied to predict future values of the silicon content. It is demonstrated that the method is capable to develop models, among which a proper choice can be made for prediction. The potential of multi-step predictions is also studied. Finally, some conclusions concerning the method and the results are drawn.
