Bayesian-based probability and time series methods allow data scientists to adapt their models to uncertainty and better predict outcomes. In this series of liveProjects, you’ll take on the role of a data scientist making customer predictions for hotels and airlines. You’ll use ARIMA, Bayesian dynamic linear modeling, PyMC3 and TensorFlow Probability to model hotel booking cancelations, and implement a Prophet model with uncertainty analysis to forecast air passenger numbers. Each project in the series is focused on a different time series forecasting model, allowing you to compare model performance and choose the skills most relevant to your career development.
This liveProject is for data analysts with a basic understanding of time series methods and data manipulation tools in Python including pandas. To begin this liveProject, you will need to be familiar with the following:
In this liveProject, you’ll learn how to implement a time series forecast through analysis of seasonality and trend patterns, along with proper configuration of ARIMA model parameters.