PyFlux is an open source time series library for Python. The library has a vast array of modern time series models, as well as a flexible array of inference options (frequentist and Bayesian) that can be applied to these models. By combining breadth of models with breadth of inference, PyFlux allows for a probabilistic approach to time series modelling.
See some examples, model explanations and documentation at PyFlux.com. PyFlux is still only alpha software and can become much better and faster with your contributions! - see here for guidelines on how you can get involved.
Update : November release is incoming; scheduled release date is November 13. Many new features and models, increased stability, rewrite of the distribution API, new documentation and tutorials, and plenty more unit tests...
- ARIMA models
- ARIMAX models
- Dynamic Autoregression models
- GARCH models
- Beta-t-EGARCH models
- EGARCH-in-mean models
- EGARCH-in-mean regression models
- Long Memory EGARCH models
- Skew-t-EGARCH models
- Skew-t-EGARCH-in-mean models
- GAS models
- GASX models
- GAS State Space models
- GP-NARX models
- Gaussian State Space models
- Neural Network AR models
- Non-Gaussian State Space models
- VAR models
- Bayesian VAR models
- Black Box Variational Inference
- Laplace Approximation
- Maximum Likelihood and Penalized Maximum Likelihood
- Metropolis-Hastings
pip install pyflux
Supported on Python 2.7 and 3.5.
- PyData San Francisco 2016 - August 2016 - a tour of time series (and predicting NFL games)
- PyData London Meetup - June 2016 - an introduction to the library in its early stages
PyFlux is still alpha software so results should be treated with care, but citations are very welcome:
Ross Taylor. 2016. PyFlux: An open source time series library for Python http://www.pyflux.com
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