06-06, 09:00–10:30 (Europe/London), Doddington Forum
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.
Traditional time series methods often struggle with complex patterns, uncertainty quantification, and incorporating prior knowledge. Bayesian methods offer a robust alternative, providing a flexible framework for handling these challenges. This tutorial will equip participants with the skills to leverage the power of Bayesian time series analysis using PyMC.
This tutorial is designed for data scientists, analysts, and researchers with some familiarity with Python and basic statistical concepts. Prior experience with time series analysis is helpful but not strictly required. A basic understanding of probability distributions and Bayesian inference will be beneficial, but we will review key concepts. Participants should be comfortable working with Jupyter notebooks.
By the end of this tutorial, participants will be able to:
- Understand the advantages of Bayesian time series analysis.
- Implement various Bayesian time series models using PyMC.
- Preprocess time series data for Bayesian modeling.
- Perform model selection and comparison.
- Evaluate model fit and diagnose potential issues.
- Generate forecasts and interpret results.
- Apply Bayesian time series methods to real-world datasets.
No previous knowledge expected
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.