  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.
