Tutorial
**State Space Models** (SSMs) are powerful tools for time series analysis, widely used in finance, economics, ecology, and engineering. They allow researchers to encode structural behavior into time series models, including *trends*, *seasonality*, *autoregression*, and *irregular fluctuations*, to name just a few. Many workhorse time series models, including ARIMA, VAR, and ETS, are special cases of the general statespace framework. In this practical, hands-on tutorial, attendees will **learn how to leverage PyMC's new state-space modeling** capabilities (`pymc_extras.statespace`) to build, fit, and interpret Bayesian state space models. Starting from fundamental concepts, we'll **explore several real-world use cases**, demonstrating how SSMs help tackle common time series challenges, such as handling missing observations, integrating external regressors, and generating forecasts.
Prior experience with PyMC is not required but will be beneficial. Optional Additional Resources: - [Introduction to PyMC state space module](https://www.youtube.com/watch?v=G9VWXZdbtKQ) - [Podcast episode on PyMC's state space module](https://learnbayesstats.com/episode/124-state-space-models-structural-time-series-jesse-grabowski) - [PyMC State Space Module GitHub Repository](https://github.com/pymc-devs/pymc-extras/tree/main/pymc_extras/statespace)
State Space Models offer a structured yet flexible framework for time series analysis. They elegantly handle latent processes like trends, seasonality, and noisy observations, making them particularly valuable in real-world applications.
We'll start with a brief overview of the theory behind SSMs, followed by practical examples where participants will:
This tutorial is aimed at data scientists, statisticians, and data analysts with a basic understanding of statistics and Python, who are interested in expanding their toolkit with Bayesian time series methods. Prior experience with PyMC is not required but will be beneficial.
By the end of this tutorial, attendees will:
Basic understanding of probability and statistics, and familiarity with Python. Prior experience with PyMC is not required but will be beneficial.
All tutorial materials, including notebooks and datasets, will be made available via a GitHub repository.
0 - 10 min: Introduction to State Space Models
10 - 25 min: State Space Model Fundamentals
25 - 55 min: Implementing SSMs with PyMC (Hands-On)
55 - 75 min: Advanced State Space Modeling (Hands-On)
75 - 85 min: Real-world Application Case Study
85 - 90 min: Wrap-up and Interactive Q&A
We believe this tutorial will empower participants with practical knowledge of state space modeling in PyMC, enabling them to effectively analyze complex time series data using Bayesian approaches.
Senior Applied Scientist
⚾ Senior Applied Scientist @ Miami Marlins 🎙️ Creator @ LearnBayesStats Podcast 📊 Cofounder @ PyMC Labs 👨🏫 Teacher @ Intuitive Bayes
Jesse Grabowski is a PhD candidate at Paris 1 Pantheon-Sorbonne. He is also a principal data scientist at PyMC labs, and a core developer of PyMC, Pytensor, and related packages. His area of research includes time series modeling, macroeconomics, and finance.