A Python package for Bayesian time series forecasting and inference
orbit
A Python package for Bayesian forecasting with object-oriented design and probabilistic models under the hood.
This project
- is stable and being incubated for long-term support. It may contain new experimental code, for which APIs are subject to change.
- requires PyStan as a system dependency. PyStan is licensed under GPLv3, which is a free, copyleft license for software.
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.
Currently, it supports concrete implementations for the following
models:
- Exponential Smoothing (ETS)
- Damped Local Trend (DLT)
- Local Global Trend (LGT)
- Kernel Time-based Regression (KTR-Lite)
It also supports the following sampling methods for
model estimation:
- Markov-Chain Monte Carlo (MCMC)