aeon is an open-source Python toolkit for time series machine learning algorithms,
evaluation, and other time series related utilities. It based on the scikit-learn
interface and is developed by a community of researchers and practitioners from a range
of backgrounds. The GitHub repository for aeon is here,
which contains links to our project documentation and communication channels.
As an affiliated project, aeon is putting forward Google Summer of Code (GSoC) 2025
project ideas under the NumFOCUS umbrella. We recommend that
prospective applicants look at the aeon contributor guide
and good first issues.
Feel free to post and ask questions on our dedicated GSoC 2025 discussion
or on our community Slack.
The projects presented here are our top picks for GSoC 2025. See our general projects page for more project ideas. We are open for discussion if you are interested in another project, but we recommend that you contact us early to discuss your ideas.
This project involves implementing clustering algorithms with integration to our
distances module.
Complexity: High
Duration 350 hours
Mentors: Chris Holder (@chrisholder), Matthew Middlehurst (@MatthewMiddlehurst) and Tony Bagnall (@TonyBagnall)
Clustering algorithms making use of distance measures are popular approaches for time
series clustering. While well-known measures such as Euclidean Distance (ED) are used
for feature vector clustering, algorithms can be adapted for time series data by using
elastic distances such as Dynamic Time Warping (DTW) to compare two series. The aeon
implementation of various distance measures and clustering algorithms were recently
used to evaluate the effectiveness of different elastic distances for clustering [1].
While the aeon distances module is already extensive, there are still clusterers
that could be implemented. aeon currently has common algorithms such as KMeans and
KMedoids, but is missing algorithms such as Density Peaks [2], DBSCAN [3] and
Hierarchical clustering approaches. The project will involve implementing and evaluating
some of these, and ensuring they are properly integrated to use the wide variety of
functions in the distances module.
- Python 3
- Git and GitHub
- Basic understanding of machine learning, specifically clustering algorithms and distance measures
Optional but useful skills:
- Writing code using
numba - For understanding the alternative implementations: Java
- A Python implementation of one or more algorithms using the
aeontime series classification API. - An evaluation of the mentees implementation against alternative implementations of the algorithm, showing that the implementation is as accurate and efficient enough that it is feasible to run experiments with.
- Holder, C., Middlehurst, M. and Bagnall, A., 2024. A review and evaluation of elastic distance functions for time series clustering. Knowledge and Information Systems, 66(2), pp.765-809.
- Rodriguez, A. and Laio, A., 2014. Clustering by fast search and find of density peaks. science, 344(6191), pp.1492-1496.
- Ester, M., Kriegel, H.P., Sander, J. and Xu, X., 1996, August. A density-based algorithm for discovering clusters in large spatial databases with noise. In kdd (Vol. 96, No. 34, pp. 226-231).
Complexity: High
Duration 350 hours
Mentors: Tony Bagnall (@TonyBagnall) and Matthew Middlehurst (@MatthewMiddlehurst)
This project will investigate algorithms for forecasting based on traditional machine
learning (tree based) and time series machine learning (transformation based). Note
this project will not involve deep learning based forecasting. It will involve
helping develop the aeon framework to work more transparently with ML algorithms,
evaluating regression algorithms already in aeon[1] for forecasting problems [2] and
implementing at least one algorithm from the literature not already in aeon, such as
SETAR-Tree [3].
- Python 3
- Git and GitHub
- Basic understanding of forecasting.
- Basic understanding of machine learning, specifically decision trees.
- Contributions to the
aeonforecasting module. - Implementation of a machine learning forecasting algortihms.
- Help write up results for a technical report/academic paper (depending on outcomes).
- Guijo-Rubio, D., Middlehurst, M., Arcencio, G., Silva, D.F. and Bagnall, A., 2023. Unsupervised feature based algorithms for time series extrinsic regression. arXiv preprint arXiv:2305.01429.
- https://forecasters.org/resources/time-series-data/
- Godahewa, R., Webb, G.I., Schmidt, D. and Bergmeir, C., 2023. SETAR-Tree: a novel and accurate tree algorithm for global time series forecasting. Machine Learning, pp.1-37.
Complexity: High
Duration 350 hours
Mentors: Ali Ismail-Fawaz (@hadifawaz1999), Tony Bagnall (@TonyBagnall) and Matthew Middlehurst (@MatthewMiddlehurst)
Time series forecasting plays a crucial role in numerous domains, including finance, healthcare,
energy, and climate science. This project aims to integrate deep learning-based time series forecasting
models into aeon. By leveraging state-of-the-art architectures such as Transformers [1], Temporal
Convolutional Networks (TCNs) [2], and Long Short-Term Memory (LSTM) [3] networks, the project will
enhance aeon's forecasting capabilities. The goal is to provide scalable, efficient, and
user-friendly deep learning models tailored for time series applications, expanding
aeon's functionality and usability for researchers and practitioners.
- Python 3
- Experience with TensorFlow or PyTorch for building and training deep learning models.
- Git and Github
Optional but useful skills:
- Basic knowledge about Time Series Forecasting is appreciated
- A python framework for deep learning models as a submodule of the aeon time series forecasting module.
- Integration the models' neural network part into the networks module of aeon.
- Integration of features similar to existing deep learning modules in aeon such as saving and loading models.
- Testing the developped models with all their features and attend a high coverage.
- Developing notebooks for deep time series forecasting.
- Zhou, Haoyi, et al. "Informer: Beyond efficient transformer for long sequence time-series forecasting." Proceedings of the AAAI conference on artificial intelligence. Vol. 35. No. 12. 2021.
- Bai, Shaojie, J. Zico Kolter, and Vladlen Koltun. "An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv." arXiv preprint arXiv:1803.01271 10 (2018).
- Salinas, David, et al. "DeepAR: Probabilistic forecasting with autoregressive recurrent networks." International journal of forecasting 36.3 (2020): 1181-1191.
Complexity: Medium
Duration 90 or 175 hours
Mentors: Matthew Middlehurst (@MatthewMiddlehurst), Tony Bagnall (@TonyBagnall) and Antoine Guillaume (@baraline)
The aeon documentation is a key resource for users of the toolkit. It provides
information on how to install the toolkit, how to use the toolkit, and how to
contribute to the toolkit. The aeon documentation is built using sphinx and hosted
on readthedocs.
While there are always improvements that can be made to the general documentation itself (e.g., improving the clarity of the text, adding more examples, etc.) for both webpages and estimator docstrings, this project focuses on implementing functions to automatically link relevant API pages together and ensure new pull requests are accompanied by the appropriate documentation. Some examples of improvements that could be made include:
- Linking to examples to in API pages where the function/class is used similar to
scikit-learn(e.g., here) - Improving the estimator overview page by further integrating the tags system or adding search and filtering functionality
- Implementing workflows to ensure that new public functionality includes a valid docstring (i.e. has a description, parameters, returns, etc. sections where relevant)
There is a lot of potential for additional functionality, so feel free to suggest improvements or new features outside the examples provided.
- Python 3
- Git and GitHub
- Understanding of how
sphinxis used to build documentation
Optional but useful skills:
- Understanding of GitHub Actions and writing workflows
- At least one new feature or improvement to the
aeondocumentation webpage (outside of general text improvements)
And/Or (depending on project duration scope)
- Improvement to the
aeontesting suite to ensure that new PRs are accompanied by the appropriate documentation
Complexity: Medium
Duration 90 or 175 hours
Mentors: Matthew Middlehurst (@MatthewMiddlehurst), Tony Bagnall (@TonyBagnall) and Antoine Guillaume (@baraline)
This project involves updating the aeon linting and type checking workflows to
use modern tools and ensure that the codebase is up to date with the latest
Python standards.
The aeon toolkit uses pre-commit to run code quality checks on all code changes
and ensure that they meet the project's standards. This includes a number of checks and
formatting tools, such as black, flake8, and isort (see here).
Over time new tools have been released such as ruff and tools we previously used such
as pydocstyle have been deprecated. The first part of this project will involve
modernising the pre-commit configuration to use the latest tools.
aeon contributors have been encouraged to add type hints to the codebase, but this
is a gradual process and there are still many parts of the codebase that are not fully
typed. A big issue we face in this is the current lack of automated testing to ensure
that implemented type hints are accurate. This second part project will involve
implementing robust testing utilities to help contributors and reviewers ensure that
new type hints are correct.
Other ideas to improve the code quality testing in aeon pull requests or deliver
feedback from tests to contributors are welcome.
- Python 3
- Git and GitHub
- Understanding of GitHub Actions and writing workflows
- Updated workflows for checking code quality in
aeonpull requests - Automated testing and utilities to help contributors implement accurate type hints
for
aeoncode.