Dynamic Network Models for Forecasting
This work addresses forecasting challenges in domains like economics, but it appears incremental as it builds on existing belief network and time-series methods.
The authors tackled the problem of probabilistic forecasting by developing a dynamic network model (DNM) that integrates belief networks and time-series analysis, extending static models to handle temporal dependencies for applications like forecasting U.S. car sales in Japan.
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.