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.
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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:
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Intermediate knowledge of Python, particularly pandas, NumPy
Intermediate knowledge of scikit-learn
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Intermediate time series methodologies
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