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.
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