Skip to content

Practical introduction to modelling and testing for structural breaks in time-series data.

Notifications You must be signed in to change notification settings

avisionh/analysis-structuralbreak

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

61 Commits
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Training: Time-Series

Welcome to this repository of training materials for analysing time-series data.

The training materials will be written in Python and hosted via Jupyter Book.

Who are these training materials for?

These training materials are designed with data practitioners in mind.

In particular, it is created from this perspective to empower readers to immediately begin modelling and forecasting time-series data.

How are the training materials organised?

The book will introduce some standard time-series theory briefly and focus on the practical introduction of analysing and modelling it.

It particular, it will cover topics such as:

  • Compositional data
  • Stationarity
  • ARIMA modelling

About

Practical introduction to modelling and testing for structural breaks in time-series data.

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published