Forecasting with Bayesian Modeling

Bayesian Statistical Methods with PyMC3 you own this product

This project is part of the liveProject series Time Series Forecasting with Bayesian Modeling
intermediate Python (knowledge of pandas, NumPy, scikit-learn) • basics of time series methodologies
skills learned
manipulating a dataset with pandas • probabilistic time series analysis with PyMC3 • generating posterior distributions • modeling autoregressive processes
Michael Grogan
1 week · 4-6 hours per week · ADVANCED

pro $24.99 per month

  • access to all Manning books, MEAPs, liveVideos, liveProjects, and audiobooks!
  • choose one free eBook per month to keep
  • exclusive 50% discount on all purchases

lite $19.99 per month

  • access to all Manning books, including MEAPs!


5, 10 or 20 seats+ for your team - learn more

Look inside
In this liveProject, you’ll use PyMC3 to generate a posterior distribution of hotel cancellations. This will allow you to build a predictive model that can update its predicted probabilities by incorporating new information. You’ll master methods for modelling mean and standard deviations based on the selected prior values, generating distributions to determine if data follows an AR(1) process, modeling of stochastic volatility and generalized linear modelling with PyMC3.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

book resources

When you start your liveProject, you get full access to the following books for 90 days.

project author

Michael Grogan
Michael Grogan is a data scientist with expertise in TensorFlow and time series analysis. His educational background is a Master's degree in Economics from University College Cork, Ireland. As such, much of his work has been in the domain of business intelligence, i.e. using machine learning technologies to develop solutions to a wide range of business problems. He has implemented time series solutions for organizations across a range of industries through the implementation of statistical analysis as well as more advanced machine learning methodologies. In addition, he has delivered numerous seminars and training courses in the areas of data science and machine learning, including for Manning and O'Reilly Media.


This liveProject is for data analysts with an intermediate understanding of time series analysis. To begin this liveProject, you will need to be familiar with:

  • Intermediate Python knowledge (pandas, NumPy)
  • Intermediate scikit-learn
  • Intermediate probability
  • Intermediate statistical theory
  • Intermediate time series methodologies

you will learn

In this liveProject, you’ll learn to generate Bayesian models based on numerous components of a dataset.

  • Generating posterior distributions
  • Modeling autoregressive processes
  • Volatility modeling
  • Bayesian generalized linear modeling


You choose the schedule and decide how much time to invest as you build your project.
Project roadmap
Each project is divided into several achievable steps.
Get Help
While within the liveProject platform, get help from other participants and our expert mentors.
Compare with others
For each step, compare your deliverable to the solutions by the author and other participants.
book resources
Get full access to select books for 90 days. Permanent access to excerpts from Manning products are also included, as well as references to other resources.

choose your plan


only $41.67 per month
  • five seats for your team
  • access to all Manning books, MEAPs, liveVideos, liveProjects, and audiobooks!
  • choose another free product every time you renew
  • choose twelve free products per year
  • exclusive 50% discount on all purchases
  • Bayesian Statistical Methods with PyMC3 project for free