Dynamic network models for forecasting

Authors:
Paul Dagum;Adam Galper;Eric Horvitz
Affiliations:
Medical Informatics, Stanford University School of Medicine, Stanford, California and Polo Alto Laboratory, Rockwell International Science Center, Palo Alto, California;Medical Informatics, Stanford University School of Medicine, Stanford, California;Medical Informatics, Stanford University School of Medicine, Stanford, California and Polo Alto Laboratory, Rockwell International Science Center, Palo Alto, California
Venue:
UAI'92 Proceedings of the Eighth international conference on Uncertainty in artificial intelligence
Year:
1992

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Abstract

We have developed a probabilistic forecasting methodology through a synthesis of belief-network models and classical time-series analysis. We present the dynamic network model (DNM) and describe methods for constructing, refining, and performing inference with this representation of temporal probabilistic knowledge. The DNM representation extends static belief-network models to more general dynamic forecasting models by integrating and iteratively refining contemporaneous and time-lagged dependencies. We discuss key concepts in terms of a model for forecasting U.S. car sales in Japan.