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 PyStan 2.0.

Citation

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

Orbit: Probabilistic Forecast with Exponential Smoothing

Bibtex:

@misc{
    ng2020orbit,
    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]