Department of Statistical Science
Duke University
presents:
Emma Sarno
Università di Napoli Federico II, Naples, Italy
"A Dynamic Model Selection Procedure To Forecast Using
Multi-Process Models"
Abstract: Multi-process models are particularly useful when observations appear extreme relative to their forecasts, because they allow for explanations of any behavior of a time series, considering more generating sources simultaneously. In my work, the multi-proc ess 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 this talk, different treatments of outliers and struct ural 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 of the overall predictive ability of multi-process models, whenever ano malous observations occur.
KEY WORDS: Dynamic Linear Models, Multi-Process Models, Robustness Sensitivity, Outliers, Structural changes, CP6 dataset
November 1, 1996
4:00 pm - 5:00 pm
130 Soc/Psych Building Any questions concerning the seminar may be addressed to Cheryl McGhee @[919] 684-8029, e-mail cheryl@stat.duke.edu