# Effective Bayesian Modeling of Groups of Related Count Time Series

arXiv: https://arxiv.org/abs/1405.3738 · HTML: https://mlsanity.com/paper/1405.3738/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 May 2014.

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

- [TCCT: Tightly-Coupled Convolutional Transformer on Time Series Forecasting](https://mlsanity.com/paper/2108.12784/) (2021)
- [Forecasting intermittent time series with Gaussian Processes and Tweedie likelihood](https://mlsanity.com/paper/2502.19086/) (2025)
- [C2FAR: Coarse-to-Fine Autoregressive Networks for Precise Probabilistic Forecasting](https://mlsanity.com/paper/2312.15002/) (2023)

## Abstract (arXiv)

Time series of counts arise in a variety of forecasting applications, for which traditional models are generally inappropriate. This paper introduces a hierarchical Bayesian formulation applicable to count time series that can easily account for explanatory variables and share statistical strength across groups of related time series. We derive an efficient approximate inference technique, and illustrate its performance on a number of datasets from supply chain planning.