About Orbit

Orbit is a Python package for Bayesian time series modeling and inference. It provides a familiar and intuitive initialize-fit-predict interface for working with time series tasks, while utilizing probabilistic programing languages under the hood.

Currently, it supports the following models:

  • Damped Local Trend (DLT)

  • Exponential Smoothing (ETS)

  • Local Global Trend (LGT)

  • Kernel-based Time-varying Regression (KTR)

It also supports the following sampling methods for model estimation:

  • Markov-Chain Monte Carlo (MCMC) as a full sampling method

  • Maximum a Posteriori (MAP) as a point estimate method

  • Stochastic Variational Inference (SVI) as a hybrid-sampling method on approximate distribution

Under the hood, the package is leveraging probabilistic program such as pyro and cmdstanpy.


To cite Orbit in publications, refer to the following whitepaper:

Orbit: Probabilistic Forecast with Exponential Smoothing


    title={Orbit: Probabilistic Forecast with Exponential Smoothing},
    author={Edwin Ng,
        Zhishi Wang,
        Huigang Chen,
        Steve Yang,
        Slawek Smyl
    year={2020}, eprint={2004.08492}, archivePrefix={arXiv}, primaryClass={stat.CO}

Blog Post

1. Introducing Orbit, An Open Source Package for Time Series Inference and Forecasting [ Link] 2. The New Version of Orbit (v1.1) is Released: The Improvements, Design Changes, and Exciting Collaborations [ Link]