Forecasting with Bayesian Modeling

Data Manipulation and ARIMA Modeling with Pyramid

This free project is part of the liveProject series Time Series Forecasting with Bayesian Modeling.
prerequisites
intermediate knowledge of Python (particularly pandas) • basics of time series methodologies
skills learned
dataset manipulation with pandas • time series forecasting with pmdarima
Michael Grogan
1 week · 4-6 hours per week · ADVANCED
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In this liveProject, you’ll investigate seasonality in hotel cancellations by building an ARIMA model that can predict cancellations on a weekly basis. You’ll learn how to manipulate a dataset with pandas in order to form a weekly time series, before going on to make your first predictions.

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 time series methods and data manipulation tools in Python including pandas. To begin this liveProject, you will need to be familiar with:

TOOLS
  • Intermediate knowledge of Python, particularly the pandas, NumPy, and sklearn libraries

  • TECHNIQUES
    • Intermediate time series methodologies

you will learn

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.

  • Manipulate a dataset with pandas
  • Implement time series forecasts with pmdarima

features

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