# GSoC 2025 - ML Forecasting Tina
###### tags: `aeon-gsoc`

__Contributor:__ [Tina Jin](https://github.com/TinaJin0228)
__GSoC page:__ https://summerofcode.withgoogle.com/organizations/numfocus/projects/details/MPYRSOTi
__Project:__ aeon - Implementing and Evaluating Machine Learning Forecasters
__Project length:__ 12 weeks
__Mentors:__ Matthew Middlehurst, Tony Bagnall
__Mid-project evaluation:__ July 14
__Final evaluation:__ September 1
__Blog link:__ https://medium.com/@jintina48/list/gsoc25-blog-11a0081fc6e2
__Regular meeting time:__ 9:30 Monday UTC
__Meeting time availability:__ 8:00 - 14:00 UTC

## Project Summary

This project will investigate algorithms for forecasting based on traditional machine learning (tree based) and time series machine learning (transformation based). It will involve helping develop the aeon framework to process both standard ML and extrisnic regression algorithms for forecasting. This will involve evaluating regression algorithms already in `aeon` for forecasting problems as well as `scikit-learn` regressors. The tree-based SETAR-Tree and SETAR-Forest algorithms will also be implemented in the forecasting module.

### Wish list of algorithms

[SETAR-Tree/SETAR-Forest](https://link.springer.com/article/10.1007/s10994-023-06316-x)

## Project Timeline

Issues: [#2816](https://github.com/aeon-toolkit/aeon/issues/2816) (SETAR-Tree/SETAR-Forest)

### June 2nd: Week 1-2

- Implement the basic SETAR-Tree class structure.
- If viable, create separate TAR/SETAR forecaster
- Add testing and documentation as the class develops

### June 16th: Week 3-4

- Finish SETAR-Tree implementation
- Implement SETAR-Forest extension
- Add to API and ensure broad testing coverage

### June 30th: Week 5-6

- Run SETAR-Tree/Forest on datasets from the publication and the ensure results are comparable
- Write/extend forecasting notebook to include usage of models such at SETAR-Tree and ETS (as a new notebook or maybe extending [current](https://github.com/aeon-toolkit/aeon/blob/main/examples/forecasting/forecasting.ipynb))

### Mid-project Deliverables

- Implementation of SETAR-Tree with documentation and testing
- Implementation of SETAR-Forest with documentation and testing
- Notebook (or notebook section) on forecasting for SETAR-Tree

### Week 7-8

- Review and implement transformations which will be useful when using the RegressionForecaster class i.e. detrending and deseasonalising
- good to be able to reproduce some of the M4 pipelines i.e. https://github.com/mloning/sktime-m4/blob/master/scripts/construct.py#L88 and add a test to make sure we can do this
- Review the implementation of the pipeline forecasting module.
- Discuss how to improve RegressionForecaster and testing for evaluating regressors from the `regression` module

### Week 9-10

- [[ENH] Implement STL series to collection transformer #2851](https://github.com/aeon-toolkit/aeon/issues/2851)
- [[ENH] Implement MSTL forecaster #2849](https://github.com/aeon-toolkit/aeon/issues/2849)

### Week 11-12

- [[ENH] Implement TBATS forecaster #2847](https://github.com/aeon-toolkit/aeon/issues/2847)

### Final Deliverables

- SETAR-Tree and SETAR-Forest
- STL and MSTL
- TBATS
- Adapt regression forest and setar forest for exogenous
- document and notebooks

## Community Bonding Period

- [x] Introduce yourself in the community Slack channels.
- [x] Go through the contributor guide on the _aeon_ website (https://www.aeon-toolkit.org/en/stable/contributing.html).
- [x] Set up a development environment, including _pytest_ and _pre-commit_ dependencies. This will make development a lot easier for you, as you must pass the PR tests to have your code merged (https://www.aeon-toolkit.org/en/stable/developer_guide/dev_installation.html).
- [x] Review some of the important dependencies for developing aeon at a basic level:
- [x] __pytest__ for unit testing. Any code added will have to be covered by tests.
- [x] __sphinx/myst__ for documentation. Adding new functions and classes will have to be added to the API docs.
- [x] __numba__ for writing efficient functions.
- [x] Make some basic Pull Requests (PRs) to gain some experience with contributing to _aeon_ through GitHub. Some suggestions:
- [x] [Finish #2729](https://github.com/aeon-toolkit/aeon/pull/2729)
- [x] [#2823 Refactor and improve NaiveForecaster](https://github.com/aeon-toolkit/aeon/issues/2823)
- [x] [#2814 Allow exogenous variables in RegressionForecaster](https://github.com/aeon-toolkit/aeon/issues/2814)
- [ ] Read up on the subject of your project (machine learning forecasters). We will provide some literature, but we encourage you to go beyond that and ask any questions you have.
- [ ] [M4](https://www.sciencedirect.com/science/article/pii/S0169207019301128)
- [x] [SETAR-Tree](https://link.springer.com/article/10.1007/s10994-023-06316-x)
- [ ] May be helpful: [`skforecast` intro to ML forecasting](https://skforecast.org/0.7.0/introduction-forecasting/introduction-forecasting)
- [x] Decide on a project length. 12 weeks is the default but can be extended if you will be unable to work for some periods during the summer.
- [x] Refine the project timeline and deliverables with the project mentors. Agree on some milestones for both mid-project and final evaluations.
- [x] Update the GSoC webpage project to better match any new directions after discussions with mentors.
- [x] Select a tracking/blogging medium to write down and track progress made on the project. Agree on a frequency of updates.
- [x] Set up regular meeting days and times to discuss the project and track progress.

## Week 1:
## Week 2:

Good progress made on SETAR-Tree and Naive forecaster. Separate SETAR forecaster drafted.
Difficult to improve the regression forecaster
Blog: https://medium.com/@jintina48/week-1-2-gsoc-progress-report-9ca47fcae703


## Week 3:
## Week 4:

Starting work on the forest and correctness testing for SETAR tree

## Week 5:
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## Week 12: