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conda-forge.org : orbit-ml
<a href="https://github.com/uber/orbit"> <img src="https://raw.githubusercontent.com/uber/orbit/dev/docs/img/orbit-banner.png" alt="Orbit banner" /> </a> Orbit is a Python package for Bayesian time series forecasting and inference. It provides a familiar and intuitive initialize-fit-predict interface for time series tasks, while utilizing probabilistic programming languages under the hood. For details, check out our documentation and tutorials: - HTML (stable): https://orbit-ml.readthedocs.io/en/stable/ - HTML (latest): https://orbit-ml.readthedocs.io/en/latest/ Currently, it supports concrete implementations for the following models: - Exponential Smoothing (ETS) - Local Global Trend (LGT) - Damped Local Trend (DLT) - Kernel Time-based Regression (KTR) It also supports the following sampling/optimization methods for model estimation/inferences: - Markov-Chain Monte Carlo (MCMC) as a full sampling method - Maximum a Posteriori (MAP) as a point estimate method - Variational Inference (VI) as a hybrid-sampling method on approximate distribution PyPI: [https://pypi.org/project/orbit-ml/](https://pypi.org/project/orbit-ml/)
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purl: pkg:conda/orbit-ml
Keywords: arima, bayesian, bayesian-methods, bayesian-statistics, changepoint, exponential-smoothing, forecast, forecasting, machine-learning, orbit, probabilistic, probabilistic-programming, pyro, pystan, python, pytorch, regression, regression-models, stan, time-series
License: Apache-2.0
Latest release: about 2 years ago
First release: over 2 years ago
Stars: 1,503 on GitHub
Forks: 111 on GitHub
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Last synced: 7 days ago