Forecasting with Bayesian Modeling

Prophet Model Incorporated with Bayesian Analysis you own this product

This project is part of the liveProject series Time Series Forecasting with Bayesian Modeling
intermediate Python knowledge (pandas, NumPy) • intermediate scikit-learn
skills learned
implementing prophet models • configuring changepoints • analyzing time series data with uncertainty interval configuration
Michael Grogan
1 week · 4-6 hours per week · ADVANCED
filed under

placing your order...

Don't refresh or navigate away from the page.
liveProject This project is part of the liveProject series Time Series Forecasting with Bayesian Modeling liveProjects give you the opportunity to learn new skills by completing real-world challenges in your local development environment. Solve practical problems, write working code, and analyze real data—with liveProject, you learn by doing. These self-paced projects also come with full liveBook access to select books for 90 days plus permanent access to other select Manning products. $19.99 $29.99 you save $10 (33%)
Prophet Model Incorporated with Bayesian Analysis (liveProject) added to cart
continue shopping
adding to cart

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 eBook every time you renew
  • choose twelve free eBooks per year
  • exclusive 50% discount on all purchases
  • Prophet Model Incorporated with Bayesian Analysis eBook for free
Look inside
In this liveProject, you’ll build a Prophet model that can forecast airline passenger numbers using data from the DataSF portal. The hotel you work for believes that analyzing the travel trends of US customers will help them forecast potential travel to Europe, and bookings in the hotel. You’ll enhance your model with "changepoints" that mark a significant change in trends, and make adjustments so your model can account for uncertainty in the trend and seasonality components.
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
  • Intermediate pandas and NumPy
  • Basics of scikit-learn
  • Basics of time series methodologies

you will learn

In this liveProject, you’ll develop Prophet models to make predictions across seasonal data, and enhance the generated forecasts with the use of Bayesian techniques.

  • Prophet model implementation
  • Manual changepoint configuration
  • Analysis of uncertainty intervals


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.