A Beginner's Guide to State Space Modeling

🔊 Recorded at PyData Berlin 2025,
https://2025.pycon.de/program/GRZ3RG/

🎓 Learn how PyMC's state space models simplify Bayesian time series analysis—from AR models to Kalman filtering with real-world examples.

Speakers:
Jesse Grabowski, Alexandre Andorra

Description:
This hands-on tutorial introduces state space modeling in PyMC, demonstrating how to build powerful Bayesian time series models without wrestling with complex recursive computations. Jesse Grabowski and Alexandre Andorra guide attendees from basic AR models to advanced applications, showing how PyMC's state space module eliminates the need for manual scan operations while providing automatic Kalman filtering, smoothing, and forecasting capabilities. Through practical examples—including tracking cannonball trajectories and modeling French presidential approval ratings—the speakers demonstrate handling missing data, incorporating external regressors like unemployment rates, and building hierarchical models. The tutorial covers the transition from traditional PyMC time series approaches to the streamlined state space framework, explaining key concepts like hidden states, measurement error, and the distinction between filtered, smoothed, and predicted values. Viewers learn when to use state space models versus simpler alternatives, how to specify priors effectively, and how to interpret structural decompositions of time series data.

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Acknowledgements:
Special thanks to all the volunteers and sponsors who made this event possible.

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PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.