orbit-ml (conda): <a href="https://github.com/uber/orbit">   <img src="https://raw.githubusercontent.com/uber/orbit/dev/docs/img/orbit-banner.png" alt="Orbit banner" /> </a>  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/](https://pypi.org/project/orbit-ml/)
Keywords: arima|bayesian|bayesian-methods|bayesian-statistics|changepoint|exponential-smoothing|forecast|forecasting|machine-learning|orbit|probabilistic|probabilistic-programming|pyro|pystan|python|pytorch|regression|regression-models|stan|time-series