https://github.com/avisionh/analysis-structuralbreak
Practical introduction to modelling and testing for structural breaks in time-series data.
https://github.com/avisionh/analysis-structuralbreak
chow-test compositional-data stl structural-break-test time-series
Last synced: about 2 months ago
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Practical introduction to modelling and testing for structural breaks in time-series data.
- Host: GitHub
- URL: https://github.com/avisionh/analysis-structuralbreak
- Owner: avisionh
- Created: 2020-03-16T08:42:56.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2022-12-08T04:02:29.000Z (over 2 years ago)
- Last Synced: 2025-01-27T08:16:03.429Z (4 months ago)
- Topics: chow-test, compositional-data, stl, structural-break-test, time-series
- Homepage: https://avisionh.github.io/analysis-structuralbreak
- Size: 2.07 MB
- Stars: 2
- Watchers: 1
- Forks: 1
- Open Issues: 10
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Metadata Files:
- Readme: README.md
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README
# Training: Time-Series
Welcome to this repository of training materials for analysing time-series data.
The training materials will be written in [Python](https://www.python.org/) and hosted via [Jupyter Book](https://jupyterbook.org/intro.html).
## 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