Introducing Orbit, An Open Source Package for Time Series Inference and Forecasting
May 14, 2021 / Global
Orbit is a general interface for Bayesian time series modeling. The goal of Orbit development team is to create a tool that is easy to use, flexible, interitible, and high performing (fast computation). Under the hood, Orbit uses the probabilistic programming languages (PPL) including but not limited to Stan and Pyro for posterior approximation (i.e, MCMC sampling, SVI). Below is a quadrant chart to position a few time series related packages in our assessment in terms of flexibility and completeness. Orbit is the only tool that allows for easy model specification and analysis while not limiting itself to a small subset of models. For example Prophet has a complete end to end solution but only has one model type and Pyro has total specification model flexibility but does not give an end to end solution. Thus Orbit bridges the gap between business problems and statistical solutions.

There are many different use cases of time series forecasting at Uber, both strategic ones (long-term) and tactical ones (short-term). This Uber blog post provided an overview of those use cases. Many of them not only require end-to-end forecasting, but also a causal inference structure in order to provide explainability, quantify uncertainty, and perform a what-if scenario analysis. Orbit could improve the quality and efficiency of this process.
Orbit has a wide range of applications in Uber’s marketing data science team for measurement, planning, and forecasting. Primarily it is used in measuring the performance for various marketing levers at subchannel and daily granularity.
Orbit enables the easy decomposition of a KPI time series into trend, seasonality, and marketing channels effects. This decomposition enables unbiased forecasting and dynamic insights, including cost curves and ROAS of marketing channels. The forecasting is an important part of planning future marketing budgets and the optimization of spending across different channels and regions.
Orbit can utilize real world data in multiple formats simultaneously; i.e., incorporating results from previous experimentations along with multiple channels of contemporaneous data.