uber/orbit

Health v2 · maintenance only

Flags: no_license

Adoption not part of the score

2079 stars · 147 forks observed · 2026-10-02

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Orbit is a Python package for Bayesian time series forecasting and inference with an initialize-fit-predict interface. It uses probabilistic programming backends like Pyro and Stan (via cmdstanpy) under the hood.

Use cases

• forecast time series with bayesian methods
• fit exponential smoothing models in python
• predict future sales or demand with uncertainty estimates
• detect changepoints in time series data
• run bayesian regression on temporal data
• compare forecasting models like arima and ets

When to choose

• you need probabilistic forecasts with uncertainty quantification
• you want a scikit-learn-like fit/predict API for time series
• you need bayesian structural time series or exponential smoothing models

When to avoid

• you need lightweight classical forecasting without bayesian sampling overhead
• you cannot install cmdstanpy or its Stan toolchain dependencies
• you need real-time low-latency predictions rather than batch forecasting

Facets

library · maturity stable

machine-learning data-science math time-series machine-learning data-science python bayesian-forecasting time-series probabilistic-programming pyro stan exponential-smoothing changepoint-detection

Member repositories

For agents

markdown · JSON · MCP: product_card(name="uber/orbit")