PyFlux
An open-source Python library for probabilistic time series modeling with both frequentist and Bayesian inference methods.
What is PyFlux?
PyFlux is an open-source Python library for time series analysis that provides a wide array of statistical models and inference methods. It enables probabilistic time series modeling by combining traditional approaches like ARIMA and GARCH with both frequentist and Bayesian inference techniques. The library helps data scientists and researchers analyze temporal data while quantifying uncertainty in their predictions.
Data scientists, researchers, and analysts working with time series data who need flexible modeling options and probabilistic inference capabilities. Particularly useful for those in finance, economics, and scientific fields requiring sophisticated time series analysis.
PyFlux stands out by offering both breadth of time series models and breadth of inference methods in a single library, allowing users to apply probabilistic approaches to various modeling scenarios. Its combination of traditional statistical models with modern Bayesian methods provides unique flexibility for time series analysis.
Overview
Open source time series library for Python
Use Cases
Best For
- Financial time series analysis with GARCH and EGARCH models
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