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.