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 con structing, refining, and performing inference with this representation of temporal proba bilistic knowledge. The DNM representation extends static belief-network models to more general dynamic forecasting models by inte grating and iteratively refining contempora neous and time-lagged dependencies. We dis cuss key concepts in terms of a model for fore casting U.S. car sales in Japan.
Dagum et al. REF ] present a probabilistic forecasting methodology using dynamic network models, using an example model for forecasting U.S. car sales in Japan.
Dynamic Network Models for Forecasting
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Computer Science