orbit

In this article, you will learn how to use Orbit, a Python library for Bayesian time series forecasting. Orbit is a very straightforward library developed at Uber that offers an interface to train Bayesian exponential smoothing models implemented via the probabilistic programming languages Stan and Pyro. This is a practical guide: the goal here is not to go into the math behind the models, but rather to show how you can use Orbit in practice to forecast time series data using Bayesian models....

There’s a very interesting marketing mix modeling approach published by Uber’s data science team that uses coefficients that vary over time to estimate a media channel’s effects. The modeling approach is called Bayesian Time-Varying Coefficients (BTVC) and it’s available on Orbit, their forecasting package, as Kernel Time-Varying Regression. Instead of getting a single coefficient to understand each media effect, we can see how the effect varied through time with confidence intervals....