# Dynamic Network Models for Forecasting

arXiv: https://arxiv.org/abs/1303.5396 · HTML: https://mlsanity.com/paper/1303.5396/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2013.

## Built on by (most cited first, 8 of 8)

- [Fighting Money Laundering with Statistics and Machine Learning](https://mlsanity.com/paper/2201.04207/) (2022)
- [On the Limit of Explaining Black-box Temporal Graph Neural Networks](https://mlsanity.com/paper/2212.00952/) (2022)
- [Localizing Anomalies in Critical Infrastructure using Model-Based Drift Explanations](https://mlsanity.com/paper/2310.15830/) (2023)
- [Deep Gaussian Markov Random Fields for Graph-Structured Dynamical Systems](https://mlsanity.com/paper/2306.08445/) (2023)
- [ExDBN: Learning Dynamic Bayesian Networks using Extended Mixed-Integer Programming Formulations](https://mlsanity.com/paper/2410.16100/) (2024)
- [Denoising the Future: Top-p Distributions for Moving Through Time](https://mlsanity.com/paper/2506.07578/) (2025)
- [A Review of the Long Horizon Forecasting Problem in Time Series Analysis](https://mlsanity.com/paper/2506.12809/) (2025)
- [Extending the application of dynamic Bayesian networks in calculating market risk: Standard and stressed expected shortfall](https://mlsanity.com/paper/2512.12334/) (2025)

## Abstract (arXiv)

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.