Time Series Modeling with TensorFlow Probability

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prerequisites
basics of TensorFlow
skills learned
structural time series modeling • forecasting and detecting anomalies • conduct Bayesian Switchpoint Analysis
Michael Grogan
1 week · 4-6 hours per week · ADVANCED
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In this liveProject, you’ll combine the power of deep learning with probabilistic modeling. You’ll build a structural time series model that can develop probabilistic forecasts of hotel cancellations, and use this model to identify anomalies across your cancellation data. You’ll perform a similar analysis of an air passenger dataset, and then use Bayesian Switchpoint analysis to determine the approximate time interval in which searches for the term “vacation” declined.
This project is designed for learning purposes and is not a complete, production-ready application or solution.

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 a basic understanding of TensorFlow and an intermediate understanding of time series and probability methodologies. To begin this liveProject, you will need to be familiar with:

TOOLS
  • Intermediate Python knowledge (pandas, NumPy)
  • Intermediate scikit-learn
  • Basics of TensorFlow
TECHNIQUES
  • Intermediate time series methodologies
  • Intermediate probability and statistical theory

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