A hands-on PyData tutorial, by Alexandre Andorra and Jesse Grabowski
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.
This tutorial can be set up using Anaconda or Miniforge, which is a lightweight version of Anaconda that is easier to work with.
The next step is to clone or download the course materials. If you are familiar with Git, run:
git clone https://github.com/AlexAndorra/pydata-berlin-statespace-models.git
otherwise you can download a zip file of its contents, and unzip it on your computer.
The repository contains an environment.yml file with all required packages. Run:
mamba env create -f environment.yml
from the main course directory (use conda instead of mamba if you installed Anaconda). Then activate the environment:
mamba activate pydata-ssm
# or
conda activate pydata-ssm
Then, you can start JupyterLab to access the materials:
jupyter lab
For those who like to work in VS Code, you can also run Jupyter notebooks from within VS Code. To do this, you will need to install the Jupyter extension. Once this is installed, you can open the notebooks in the notebooks subdirectory and run them interactively.
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.
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:
- Understand the components of SSMs, including observation and state equations.
- Learn how to specify and fit SSMs using PyMC's state space module.
- Implement a modeling workflow using a survey data example, showing how to use SSMs to model the data and generate predictions.
- Explore advanced topics such as incorporating external regressors, generating forecasts or building custom models.
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:
- Understand the theoretical foundations of State Space Models.
- Be able to implement common SSMs (local level, trend, and seasonal models) in PyMC.
- Evaluate and interpret Bayesian state space models using PyMC.
- Appreciate practical scenarios where SSMs outperform traditional time series approaches.
Basic understanding of probability and statistics, and familiarity with Python. Prior experience with PyMC is not required but will be beneficial.
0 - 10 min: Introduction to State Space Models
- What are SSMs, and why use them?
10 - 25 min: State Space Model Fundamentals
- Observation and state equations.
- Latent states, Kalman filters, and smoothing in Bayesian frameworks.
25 - 55 min: Implementing SSMs with PyMC (Hands-On)
- Setting up a local-level model in PyMC.
- Extending models to incorporate trends and seasonality.
- Posterior inference: interpreting results and uncertainty.
55 - 75 min: Advanced State Space Modeling (Hands-On)
- Dealing with missing data and irregular intervals.
- Adding external covariates (regression components).
- Model diagnostics and posterior predictive checks.
75 - 85 min: Real-world Application Case Study
- Demonstrating an end-to-end modeling example with real data.
- Discussing best practices for practical time series modeling.
85 - 90 min: Wrap-up and Interactive Q&A
- Open floor for questions and further resources.