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
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}
}
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]