# Bayesian forecasting of multivariate time series: Scalability, structure uncertainty and decisions

arXiv: https://arxiv.org/abs/1911.09656 · HTML: https://mlsanity.com/paper/1911.09656/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Nov 2019.

## Built on by (most cited first, 2 of 2)

- [Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglement](https://mlsanity.com/paper/2301.03028/) (2023)
- [Data augmentation through multivariate scenario forecasting in Data Centers using Generative Adversarial Networks](https://mlsanity.com/paper/2201.06147/) (2022)

## Abstract (arXiv)

I overview recent research advances in Bayesian state-space modeling of multivariate time series. A main focus is on the decouple/recouple concept that enables application of state-space models to increasingly large-scale data, applying to continuous or discrete time series outcomes. The scope includes large-scale dynamic graphical models for forecasting and multivariate volatility analysis in areas such as economics and finance, multi-scale approaches for forecasting discrete/count time series in areas such as commercial sales and demand forecasting, and dynamic network flow models for areas including internet traffic monitoring. In applications, explicit forecasting, monitoring and decision goals are paramount and should factor into model assessment and comparison, a perspective that is highlighted.