Chris Fonnesbeck
Chris is a Principal Quantitative Analyst at PyMC Labs and an Adjoint Associate Professor at the Vanderbilt University Medical Center, with 20 years of experience as a data scientist in academia, industry, and government. He is interested in computational statistics, machine learning, Bayesian methods, and applied decision analysis. He hails from Vancouver, Canada and received his Ph.D. from the University of Georgia.
Sessions
Time series data is ubiquitous, from stock market prices and weather patterns to disease outbreaks and sports outcomes. Accurately modeling these data and generating useful predictions requires specialized techniques due to the unique characteristics of time series data. This tutorial provides a practical introduction to Bayesian time series analysis using PyMC, a powerful probabilistic programming library in Python. Participants will learn how to build, evaluate, and interpret various Bayesian time series models, including ARIMA models, dynamic linear models, and stochastic volatility models. We'll emphasize practical application, covering data preprocessing, model selection, diagnostics, and forecasting, empowering attendees to tackle real-world time series problems with confidence.
Join the PyMC development team for a fun and engaging hackathon!