Forecasting with Bayesian Modeling

Bayesian Dynamic Linear Modeling you own this product

This project is part of the liveProject series Time Series Forecasting with Bayesian Modeling
intermediate Python (knowledge of pandas, NumPy) • intermediate scikit-learn • basics of time series methodologies
skills learned
build a simple DLM model • build Bayesian dynamic linear models with the PyDLM library
Michael Grogan
1 week · 4-6 hours per week · ADVANCED

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In this liveProject, you’ll build a Bayesian dynamic linear model that can take account of sudden state space changes and rapidly react to dramatic trend changes. These trend changes could take many forms—from heightened demand during a major sporting event, to a global pandemic that causes cancellations to skyrocket. You’ll use the PyDLM library to generate forecasts that can dynamically adapt to the unforeseen, and quickly shift to making accurate predictions for a new reality.
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 knowledge of Python, particularly pandas, NumPy
  • Intermediate knowledge of scikit-learn
  • Intermediate time series methodologies

you will learn

In this liveProject, you’ll learn how to model dynamically changing predictors in order to make better predictions.

  • Build a simple DLM model with a defined linear trend and seasonal component
  • Update a previously built Bayesian simple linear model to include dynamic and automatic components


You choose the schedule and decide how much time to invest as you build your project.
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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.

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