# Probabilistic Reconciliation of Count Time Series

arXiv: https://arxiv.org/abs/2207.09322 · HTML: https://mlsanity.com/paper/2207.09322/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 38 references that are in the map.
- First posted on arXiv 19 Jul 2022.

## Builds on (most cited first, 2 of 2)

- [Probabilistic forecast reconciliation under the Gaussian framework](https://mlsanity.com/paper/2103.11128/) (2021)
- [A Review of Multivariate Distributions for Count Data Derived from the Poisson Distribution](https://mlsanity.com/paper/1609.00066/) (2016)

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

- [Efficient probabilistic reconciliation of forecasts for real-valued and count time series](https://mlsanity.com/paper/2210.02286/) (2022)
- [FoReco and FoRecoML: A Unified Toolbox for Forecast Reconciliation in R](https://mlsanity.com/paper/2604.27696/) (2026)

## Abstract (arXiv)

Forecast reconciliation is an important research topic. Yet, there is currently neither formal framework nor practical method for the probabilistic reconciliation of count time series. In this paper we propose a definition of coherency and reconciled probabilistic forecast which applies to both real-valued and count variables and a novel method for probabilistic reconciliation. It is based on a generalization of Bayes' rule and it can reconcile both real-value and count variables. When applied to count variables, it yields a reconciled probability mass function. Our experiments with the temporal reconciliation of count variables show a major forecast improvement compared to the probabilistic Gaussian reconciliation.