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Time Series for Python with PyFlux


PyData SF 2016

PyFlux is a new library for time series analysis for Python. It brings together a vast array of time series models, including recent models such as score-driven models and variational state space models, as well a flexible choice of inference options, including black box variational inference. In this talk I will introduce some of the features, with some fun applications to sports modelling.

This talk will introduce the PyFlux library for time series analysis in Python. I will walk through the modelling and inference options in an accessible, high-level way. I will focus on score-driven (GAS) models, which are a new flexible alternative to traditional time series models. This Python library represents the first comprehensive implementation of a GAS library, a model type that has the potential to be as widespread as ARIMA models. I will demonstrate the usefulness of the models through some fun examples in PyFlux, such as a power rating model for NFL football teams, as well as some finance examples, which I shall attempt to make fun (challenge accepted).


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