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.
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:
- Intermediate Python knowledge (pandas, NumPy)
- Intermediate scikit-learn
- Basics of TensorFlow
- Intermediate time series methodologies
- Intermediate probability and statistical theory
you will learn
In this liveProject, you’ll develop different scenario forecasts with the TensorFlow Probability library.
- Structural time series modeling
- Forecast and detect anomalies
- Bayesian Switchpoint Analysis