Date and Time

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: