# The mbsts package: Multivariate Bayesian Structural Time Series Models in R

arXiv: https://arxiv.org/abs/2106.14045 · HTML: https://mlsanity.com/paper/2106.14045/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 34 references that are in the map.
- First posted on arXiv 26 Jun 2021.

## Builds on (most cited first, 4 of 4)

- [GRATIS: GeneRAting TIme Series with diverse and controllable characteristics](https://mlsanity.com/paper/1903.02787/) (2019)
- [Multivariate Bayesian Structural Time Series Model](https://mlsanity.com/paper/1801.03222/) (2018)
- [KFAS: Exponential Family State Space Models in R](https://mlsanity.com/paper/1612.01907/) (2016)
- [Scalable Monte Carlo Inference and Rescaled Local Asymptotic Normality](https://mlsanity.com/paper/2007.00723/) (2020)

## Abstract (arXiv)

The multivariate Bayesian structural time series (MBSTS) model is a general machine learning model that deals with inference and prediction for multiple correlated time series, where one also has the choice of using a different candidate pool of contemporaneous predictors for each target series. The MBSTS model has wide applications and is ideal for feature selection, time series forecasting, nowcasting, inferring causal impact, and others. This paper demonstrates how to use the R package mbsts for MBSTS modeling, establishing a bridge between user-friendly and developer-friendly functions in the package and the corresponding methodology. Object-oriented functions in the package are explained in the way that enables users to flexibly add or deduct some components, as well as to simplify or complicate some settings.