ML Anthology

Dynamic Network Models for Forecasting

Paul Dagum, Adam Galper, Eric Horvitz
UAI 1992 pp. 41-48
doi:10.1016/B978-1-4832-8287-9.50010-4 /uai/1992/dagum1992uai-dynamic/

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.

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Text

Dagum et al. "Dynamic Network Models for Forecasting." Conference on Uncertainty in Artificial Intelligence, 1992. doi:10.1016/B978-1-4832-8287-9.50010-4

Markdown

[Dagum et al. "Dynamic Network Models for Forecasting." Conference on Uncertainty in Artificial Intelligence, 1992.](https://mlanthology.org/uai/1992/dagum1992uai-dynamic/) doi:10.1016/B978-1-4832-8287-9.50010-4

BibTeX

@inproceedings{dagum1992uai-dynamic,
  title     = {{Dynamic Network Models for Forecasting}},
  author    = {Dagum, Paul and Galper, Adam and Horvitz, Eric},
  booktitle = {Conference on Uncertainty in Artificial Intelligence},
  year      = {1992},
  pages     = {41-48},
  doi       = {10.1016/B978-1-4832-8287-9.50010-4},
  url       = {https://mlanthology.org/uai/1992/dagum1992uai-dynamic/}
}