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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
prerequisites
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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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.

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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.

prerequisites

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:

TOOLS
  • Intermediate knowledge of Python, particularly pandas, NumPy
  • Intermediate knowledge of scikit-learn
TECHNIQUES
  • 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

features

Self-paced
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.
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