# KFAS: Exponential Family State Space Models in R

arXiv: https://arxiv.org/abs/1612.01907 · HTML: https://mlsanity.com/paper/1612.01907/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Dec 2016.

## Built on by (most cited first, 3 of 3)

- [The mbsts package: Multivariate Bayesian Structural Time Series Models in R](https://mlsanity.com/paper/2106.14045/) (2021)
- [onlineforecast: An R package for adaptive and recursive forecasting](https://mlsanity.com/paper/2109.12915/) (2021)
- [Warped Dynamic Linear Models for Time Series of Counts](https://mlsanity.com/paper/2110.14790/) (2021)

## Abstract (arXiv)

State space modelling is an efficient and flexible method for statistical inference of a broad class of time series and other data. This paper describes an R package KFAS for state space modelling with the observations from an exponential family, namely Gaussian, Poisson, binomial, negative binomial and gamma distributions. After introducing the basic theory behind Gaussian and non-Gaussian state space models, an illustrative example of Poisson time series forecasting is provided. Finally, a comparison to alternative R packages suitable for non-Gaussian time series modelling is presented.