Uber Releases V1.1 of Orbit: A Python Package to Perform Bayesian Time-Series Analysis and Forecasting

Last year, the Uber team introduced Orbit, a Bayesian time series modeling user interface which is simple to use, adaptable, interoperable, and high-performing (fast computation). Orbit uses probabilistic programming languages (PPL) for posterior approximation. So far, it is the only tool that enables simple model specification and analysis without being restricted to a small number of models.

Uber Team has recently released version 1.1 of Orbit, which includes changes in the syntax of calling models, the new classes design, and the KTR (Kernel Time-varying Regression) model. Continue Reading

Github: https://github.com/uber/orbit

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