Orbit  Uber's New Open Source Python Library for Time Series Modeling
Orbit
is Uber’s new python package for time series modeling and inference using Bayesian sampling methods for model estimation.
Orbit provides a familiar and intuitive initializefitpredict interface for working with time series tasks, while utilizing probabilistic modeling under the hood.
As per Orbit’s documentation, initial release supports concrete implementation for the following models:
 Local Global Trend (LGT)
 Damped Local Trend (DLT)
Both models, which are variants of exponential smoothing, support seasonality and exogenous (timeindependent) features.
The initial release also supports the following sampling methods for model estimation:
 MarkovChain Monte Carlo (MCMC) as a full sampling method
 Maximum a Posteriori (MAP) as a point estimate method
 Variational Inference (VI) as a hybridsampling method on approximate distribution
Quick Start
Installation
pip install orbitml
Orbit requires PyStan as a system dependency. PyStan is licensed under GPLv3 , which is a free, copyleft license for software.
Data
iclaims_example
is a dataset containing the weekly initial claims for US unemployment benefits against a few related google trend queries from Jan 2010  June 2018.
Number of claims are obtained from Federal Reserve Bank of St. Louis while google queries are obtained through Google Trends API.
Quick Starter Code

