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

For details, check out our documentation and tutorials: - HTML (stable): https://orbit-ml.readthedocs.io/en/stable/ - HTML (latest): https://orbit-ml.readthedocs.io/en/latest/

Currently, it supports concrete implementations for the following models:

Exponential Smoothing (ETS)
Local Global Trend (LGT)
Damped Local Trend (DLT)
Kernel Time-based Regression (KTR)

It also supports the following sampling/optimization methods for model estimation/inferences:

Markov-Chain Monte Carlo (MCMC) as a full sampling method
Maximum a Posteriori (MAP) as a point estimate method
Variational Inference (VI) as a hybrid-sampling method on approximate distribution

PyPI: https://pypi.org/project/orbit-ml/