aeon Projects

aeon runs a range of short to medium duration projects that involve developing or using aeon and interacting with the community and the code base. These projects are designed for internships, usage as part of undergraduate/postgraduate projects at academic institutions, options for programs such as Google Summer of Code (GSoC) or just for personal side projects. For those interested in undertaking a project outside these scenarios, we recommend joining the Discord and discussing with the community.

Feel free to propose your own project ideas, but please discuss them with us first. We have an active community of researchers and students who work on aeon. Please get in touch via Discord if you are interested in any of these projects or have any questions. We will more widely advertise funding opportunities as and when they become available.

All the projects listed will require knowledge of Python and Git/GitHub. The majority of them will require some knowledge of machine learning and time series.

Current aeon projects

This is a list of some of the projects we are interested in running (last updated 25/05/2025):

Classification

1. Optimizing the Shapelet Transform for classification and similarity search

2. Improved HIVE-COTE implementation

3. Compare distance-based classification.

Clustering

1. Density peaks clustering algorithm

2. Hierarchical clustering for time series

Transformation

1. Improve ROCKET family of transformers

Visualisation

1. Explainable AI with the shapelet transform

Documentation

1. Improve automated API documentation

2. Improve the documentation tag interactivity and testing

Maintenance

1. Modernising the aeon linting and type checking workflows

Multi-module

1. Implementing multithreading for aeon estimators and tools for evaluating multithreading performance

Classification

1. Optimizing the Shapelet Transform for Classification and Similarity Search

Contact: Antoine Guillaume (@baraline) and Tony Bagnall (@TonyBagnall)

Related Issues

#186 #973 #1322

Description

A shapelet is defined as a time series subsequence representing a pattern of interest that we wish to search for in time series data. Shapelet-based algorithms can be used for a wide range of time series tasks. In this project, we will focus on its core application, which is to create an embedding of the input time series.

Our goal in this project will be to optimize the code related to the shapelet transform method, which takes as input a set of shapelets and a time series dataset, and give as output a tabular dataset containing the features characterizing the presence (or absence) of each shapelet in the input time series (more information in [1] and [2]).

Similarity search is another field of time series, which has proposed greatly optimized algorithms (see [3] and [4]) for the task of finding the best matches of a subsequence inside another time series. As this task is extremely similar to what is done in the shapelet transform, we want to adapt these algorithms to the context of shapelets, in order to achieve significant speed-ups.

Project stages

To achieve this goal, with the assistance of the mentor, we identify the following steps for the mentee:

1. Learn about aeon best practices, coding standards and testing policies.

2. Study the shapelet transform algorithm and how it is related to the task of similarity search.

3. Study the similarity search algorithms for the Euclidean distance and the computational optimization they use.

4. Propose a candidate implementation for to increase the performance of the computations made by a single shapelet. This can be made with the help of the existing implementation of the similarity search module in aeon.

5. Measure the performance of this first candidate implementation against the current approach.

6. Implement this solution to the shapelet transform algorithm, which uses multiple shapelets.

7. Benchmark the implementation against the original shapelet transform algorithm.

8. If time, generalize this new algorithm to the case of dilated shapelets (see [5]).

References

1. Hills, J., Lines, J., Baranauskas, E., Mapp, J. and Bagnall, A., 2014. Classification of time series by shapelet transformation. Data mining and knowledge discovery, 28, pp.851-881.

2. Bostrom, A. and Bagnall, A., 2017. Binary shapelet transform for multiclass time series classification. Transactions on Large-Scale Data-and Knowledge-Centered Systems XXXII: Special Issue on Big Data Analytics and Knowledge Discovery, pp.24-46.

3. Yeh, C.C.M., Zhu, Y., Ulanova, L., Begum, N., Ding, Y., Dau, H.A., Silva, D.F., Mueen, A. and Keogh, E., 2016, December. Matrix profile I: all pairs similarity joins for time series: a unifying view that includes motifs, discords and shapelets. In 2016 IEEE 16th international conference on data mining (ICDM) (pp. 1317-1322). Ieee.

4. Zhu, Y., Zimmerman, Z., Senobari, N.S., Yeh, C.C.M., Funning, G., Mueen, A., Brisk, P. and Keogh, E., 2016, December. Matrix profile ii: Exploiting a novel algorithm and gpus to break the one hundred million barrier for time series motifs and joins. In 2016 IEEE 16th international conference on data mining (ICDM) (pp. 739-748). IEEE.

5. Guillaume, A., Vrain, C. and Elloumi, W., 2022, June. Random dilated shapelet transform: A new approach for time series shapelets. In International Conference on Pattern Recognition and Artificial Intelligence (pp. 653-664). Cham: Springer International Publishing.

2. Improved HIVE-COTE implementation

Contact: Matthew Middlehurst (@MatthewMiddlehurst) and Tony Bagnall (@TonyBagnall)

Related Issues

#663 #1646

Description

The Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE) [1,2,3] is a time series classifier that has claims to be state-of-the-art, particularly in terms of probabilistic estimates [4]. There have been several iterations that use different base classifiers but they all share the same design basic: classifiers using different representations are combined in a weighted meta-ensemble [4]. There are two HIVE-COTE implementations currently in aeon: HIVE-COTEV1 [2] and HIVECOTEV2 [3]. This project will involve combining these into a single framework, modularising the ensemble stage and possibly experimenting with alternative structures.

Project stages

1. Learn about aeon best practices, coding standards and testing policies.

2. Study the HIVE-COTE algorithm and previous aeon implementation.

3. Implement a modular HIVE-COTE or composable classification ensemble framework.

4. Convert HIVE-COTEV1 and HIVE-COTEV2 classifiers to use this framework ensuring results remain the same.

5. Restructure the ensemble stage to allow easy experimentation with variants.

References

1. A Bagnall, J Lines, J Hills, A Bostrom, Time-series classification with COTE: the collective of transformation-based ensembles, IEEE Transactions on Knowledge and Data Engineering 27 (9), 2522-2535

2. M Middlehurst, J Large, M Flynn, J Lines, A Bostrom, A Bagnall, HIVE-COTE 2.0: a new meta ensemble for time series classification, Machine Learning 110 (11), 3211-3243

3. J Lines, S Taylor, A Bagnall, Time series classification with HIVE-COTE: The hierarchical vote collective of transformation-based ensembles, ACM Transactions on Knowledge Discovery from Data (TKDD) 12 (5), 1-35

4. Middlehurst, M., Schäfer, P. and Bagnall, A., 2024. Bake off redux: a review and experimental evaluation of recent time series classification algorithms. Data Mining and Knowledge Discovery, 38(4), pp.1958-2031.

3. Compare distance-based classification and regression

Contact: Chris Holder (@chrisholder) and Tony Bagnall (@TonyBagnall)

Related Issues

#424 #425 #426 #427 #488

Description

Distance-based algorithms are popular for time series classification and regression. However, the evaluation of distance functions for classification have not comprehensively covered all possible uses. For example, there has not been a proper bake off for using elastic distance with support vector machines or with tuning distance functions and classifiers in combination. This project will combine implementing alternative distance functions and comparing performance on the UCR datasets.

Project stages

1. Learn about aeon best practices, coding standards and testing policies.

2. Study the distance model and the distance-based classifiers in aeon.

3. Read and implement alternative distance functions in the format of the aeon.distances module.

4. Test the distances against previous results if available.

5. Run a mini-bakeoff of distance-based classifiers on the UCR datasets, comparing distances using existing distance-based classifiers in aeon and scikit-learn.

Clustering

1. Density peaks clustering algorithm

Contact: Tony Bagnall (@TonyBagnall) and Chris Holder (@chrisholder).

Description

The clustering module in aeon, up until now, clusters using time series specific distance functions with partitional clustering algorithms such as k-means and k-medoids. An alternative clustering algorithm is density peaks [1]. This clustering algorithm has the benefit of not having to label all cases as cluster members, which means it can easily be adapted to anomaly detection [2]. It is a general purpose clustering algorithm that is not available in scikit learn. This project will implement the algorithm based on Java and matlab implementations then compare performance against partitional clustering for time series clustering.

Project Stages

1. Research and understand how density peaks works.

2. Implement density peaks as an aeon estimator using the aeon distances module.

3. Test the implementation against other implementations for correctness and on synthetic data used in the publication.

4. Compare against alternative TSCL algorithms.

5. Possible extensions to reflect recent research [2] with specific time series components [3].

References

1. Rodriguez, A., & Laio, A. Clustering by Fast Search and Find of Density Peaks. Science, 344 (6191), 1492-1496, 2014.

2. Chen, L., Gao, S. & Liu, B. An improved density peaks clustering algorithm based on grid screening and mutual neighborhood degree for network anomaly detection. Sci Rep 12, 1409 (2022) DOI

3. Begum et al. A General Framework for Density Based Time Series Clustering Exploiting a Novel Admissible Pruning Strategy, arXiv

2. Hierarchical clustering for time series

Contact: Tony Bagnall (@TonyBagnall) and Chris Holder (@chrisholder).

Description

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 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.

Project Stages

1. Research and understand hierarchical clustering algorithms work and different methods.

2. Implement hierarchical clustering algorithms as an aeon estimator using the aeon distances module.

3. Implement a dendrogram visualisation for the clustering.

4. Test the implementation visualisations and results against other implementations for correctness

Transformation

1. Improve ROCKET family of transformers

Contact: Ali Ismail-Fawaz (@hadifawaz1999) and Matthew Middlehurst (@MatthewMiddlehurst)

Related Issues

#313 #1126 #1248 #2179

Description

The ROCKET algorithm [1] is a very fast and accurate transformation designed for time series classification. It is based on a randomly initialised convolutional kernels that are applied to the time series and used to extract summary statistics. ROCKET has applications to time series classification, extrinsic regression and anomaly detection, but as a fast and unsupervised transformation, it has potential to a wide range of other time series tasks.

aeon has implementations of the ROCKET transformation and its variants, including MiniROCKET [2] and MultiROCKET [3]. However, these implementations have room for improvement. There is scope to speed up the implementations, and the amount of variants is likely unnecessary and could be condensed into higher quality estimators.

This projects involves improving the existing ROCKET implementations in aeon or implementing new ROCKET variants. The project will involve benchmarking to ensure that the new implementations are as fast and accurate as the original ROCKET algorithm and potentially to compare to other implementations. Besides improving the existing implementations, there is scope to implement a probabilistic ridge classifier [4] for the algorithms to use or implement GPU compatible versions of the algorithms.

Project Stages

1. Learn about aeon best practices, coding standards and testing policies.

2. Study the ROCKET, MiniROCKET, MultiROCKET algorithms and existing implementations in aeon.

3. Merge and tidy the ROCKET implementations, with the aim being to familiarise the mentee with the aeon pull request process.

4. Implement one (or more) of the proposed ROCKET implementation improvements:

   • Significantly alter the current ROCKET implementations with the goal of speeding up the implementation on CPU processing.

   • Implement a GPU version of some of the ROCKET transformers, using either tensorflow or pytorch.

   • Implement probabilistic ridge classifier as a scikit-learn estimator.

5. Benchmark the implementation against the original ROCKET implementations, looking at booth speed of the transform and accuracy in a classification setting.

References

1. Dempster, A., Petitjean, F. and Webb, G.I., 2020. ROCKET: exceptionally fast and accurate time series classification using random convolutional kernels. Data Mining and Knowledge Discovery, 34(5), pp.1454-1495.

2. Dempster, A., Schmidt, D.F. and Webb, G.I., 2021, August. Minirocket: A very fast (almost) deterministic transform for time series classification. In Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining (pp. 248-257).

3. Tan, C.W., Dempster, A., Bergmeir, C. and Webb, G.I., 2022. MultiRocket: multiple pooling operators and transformations for fast and effective time series classification. Data Mining and Knowledge Discovery, 36(5), pp.1623-1646.

4. Dempster, A., Webb, G.I. and Schmidt, D.F., 2024. Prevalidated ridge regression is a highly-efficient drop-in replacement for logistic regression for high-dimensional data. arXiv preprint arXiv:2401.15610.

Visualisation

1. Explainable AI with the shapelet transform.

Contact: TonyBagnall (@TonyBagnall) and David Guijo-Rubio (@dguijo)

Description

This project will focus on explainable AI for time series classification (TSC) [1], specifically the family of algorithms based on shapelets [2]. Shapelets are small sub certain shape of heartbeat, perhaps a short irregularity, might be useful in predicting the medical condition. We will look at the shapelet transform classifier [3]. This finds a large set of shapelets from the training data and uses them to build a classifier. We want to develop tools to help us visualise the output of the search for good shapelets to help explain why predictions are made. This project is not tied to a specific data set. It is to develop tools to help any user of the toolkit. It will involve learning about aeon and making contributions to open source toolkits, familiarisation with the shapelet code and the development of a visualisation tool to help relate shapelets back to the training data. An outline for the project is

Project Stages

1. Familiarisation with open source, aeon and the visualisation module. Make contribution for a good first issue.

2. Understand the shapelet transfer algorithm, engage in ongoing discussions for possible improvements, run experiments to create predictive models for a test data set

3. Design and prototype visualisation tools for shapelets, involving a range of summary measures and visualisation techniques, including plotting shapelets on training data, calculating frequency, measuring similarity between

4. Debug, document and make PRs to merge contributions into the aeon toolkit.

References

1. Bagnall, A., Lines, J., Bostrom, A., Large, J. and Keogh, E. The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Mining and Knowledge Discovery, Volume 31, pages 606–660, (2017)

2. Ye, L., Keogh, E. Time series shapelets: a novel technique that allows accurate, interpretable and fast classification. Data Min Knowl Disc 22, 149–182 (2011). https://doi.org/10.1007/s10618-010-0179-5

3. Lines, L., Davis, L., Hills, J. and Bagnall, A. A shapelet transform for time series classification, KDD ‘12: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (2012) https://doi.org/10.1145/2339530.2339579

Documentation

1. Improve the documentation codebase interactions and testing

Contact: Matthew Middlehurst (@MatthewMiddlehurst)

Description

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

• 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.

Project Stages

1. Learn about aeon best practices and project documentation.

2. Familiarise with sphinx documentation generation and numpydoc docstring standards.

3. Improve the API documentation for a few classes/functions and go through the Pull Request and review process.

4. Implement at least one new feature or improvement to the aeon documentation webpage (outside of general text improvements) And/Or improve the aeon testing suite to ensure that new PRs are accompanied by the appropriate documentation.

Maintenance

Modernising the aeon linting and type checking workflows

Contact: Matthew Middlehurst (@MatthewMiddlehurst)

Description

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.

Expected Outcome(s)

1. Learn about used for code quality checks and type checking in Python.

2. Familiarise yourself with the aeon CI including pre-commit and GitHub Actions workflows.

3. Update workflows for checking code quality in aeon pull requests

4. Implement automated testing and utilities to help contributors implement accurate type hints for aeon code.

Multi-module

Implementing multithreading for aeon estimators and tools for evaluating multithreading performance

Contact: Matthew Middlehurst (@MatthewMiddlehurst)

Description

Multithreading in aeon for estimators does not have a set structure or library that must be used. Most algorithms which have an n_jobs parameter available use a mix of Joblib and numba multithreading. Algorithms which do have the capability for multithreading have not been thoroughly tested, and as such the efficiency of these implementations is unknown.

As well as expanding the amount of estimators which can use multiple threads, we would like to develop tools to evaluate whether this threading is efficient and develop documentation for contributors which want to add multithreading to aeon estimators.

Project Stages

1. Investigate Python multithreading libraries and numba multithreading.

2. Learn about aeon best practices, coding standards and how current estimators use multiple threads.

3. Write tools and testing for evaluating the efficiency of multithreaded code while maintaining single-threaded performance.

4. If any estimators are performing poorly with multithreading, implement improvements to the multithreading implementation.

5. Implement multithreading as a capability for currently lacking aeon estimators (preferably in the classification, regression and clustering modules to start).