Forecasting with Bayesian Modeling

Data Manipulation and ARIMA Modeling with Pyramid you own this product

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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Data Science

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liveProject

$19.99

liveProject This free project is part of the liveProject series Time Series Forecasting with Bayesian Modeling liveProjects give you the opportunity to learn new skills by completing real-world challenges in your local development environment. Solve practical problems, write working code, and analyze real data—with liveProject, you learn by doing.

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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

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

Self-paced: You choose the schedule and decide how much time to invest as you build your project.
Project roadmap: Each project is divided into several achievable steps.
Get Help: While within the liveProject platform, get help from other participants.
Compare with others: For each step, compare your deliverable to the solutions by the author and other participants.