# Orbit: Probabilistic Forecast with Exponential Smoothing

arXiv: https://arxiv.org/abs/2004.08492 · HTML: https://mlsanity.com/paper/2004.08492/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2020.

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

- [HierarchicalForecast: A Reference Framework for Hierarchical Forecasting in Python](https://mlsanity.com/paper/2207.03517/) (2022)
- [Deep Learning and Linear Programming for Automated Ensemble Forecasting and Interpretation](https://mlsanity.com/paper/2201.00426/) (2022)
- [Contextual Deconvolution for Variance-Stable Demand Sensing: Kernel-Modulated Operators in Promotional Retail](https://mlsanity.com/paper/2607.25664/) (2026)

## Abstract (arXiv)

Time series forecasting is an active research topic in academia as well as industry. Although we see an increasing amount of adoptions of machine learning methods in solving some of those forecasting challenges, statistical methods remain powerful while dealing with low granularity data. This paper introduces a refined Bayesian exponential smoothing model with the help of probabilistic programming languages including Stan. Our model refinements include additional global trend, transformation for multiplicative form, noise distribution and choice of priors. A benchmark study is conducted on a rich set of time-series data sets for our models along with other well-known time series models.