| Abstract: | Multi-process models are particularly useful when observations appear extreme relative to their forecasts, because they allow for explanations of any behaviour of a time series, considering more generating sources simultaneously. In this paper, the multi-process approach is extended by developing a dynamic procedure to assess the weights of the various sources, alias the prior probabilities of the rival models, that compete in the collection to make forecasts. The new criterion helps the forecasting system to learn about the most plausible scenarios for the time series, considering all the combinations of consecutive models to be a function of the magnitude of the one-step-ahead forecast error. Throughout the paper, the different treatments of outliers and structural changes are highlighted using the concepts of robustness and sensitivity. Finally, the dynamic selection procedure is tested on the CP6 dataset, showing an effective improvement in the overall predictive ability of multi-process models whenever anomalous observations occur. © 1997 John Wiley & Sons, Ltd. |
