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Orbit is a package for bayesian time series modeling and inference.

copied from cf-staging / orbit-ml

Installers

  • noarch v1.1.4.9
  • linux-64 v1.1.4.4

conda install

To install this package run one of the following:
conda install conda-forge::orbit-ml

Description

Orbit banner

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/


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