Previous aeon Projects¶
A list of projects that have been completed in the past or are currently ongoing.
2025¶
Implementing and Evaluating Machine Learning Forecasters¶
Mentors: Matthew Middlehurst (@MatthewMiddlehurst) and Tony Bagnall (@TonyBagnall)
Mentee: Tina Jin (@TinaJin0228)
https://summerofcode.withgoogle.com/organizations/numfocus/projects/details/MPYRSOTi
https://medium.com/@jintina48/list/gsoc25-blog-11a0081fc6e2
GSoC 2025 project
Deep Learning for Forecasting¶
Mentors: Tony Bagnall (@TonyBagnall) and Ali Ismail-Fawaz (@hadifawaz1999) and Matthew Middlehurst (@MatthewMiddlehurst)
Mentee: Balgopal Moharana (@lucifer4073)
https://summerofcode.withgoogle.com/organizations/numfocus/projects/details/arjEn266
https://medium.com/@lucifer4073/gsoc-25-journey-af8e3e0c2621
GSoC 2025 project
2024¶
Developing Deep Learning Framework and Implementations for Time Series Clustering¶
Mentors: Ali Ismail-Fawaz (@hadifawaz1999) and Tony Bagnall (@TonyBagnall) and Matthew Middlehurst (@MatthewMiddlehurst)
Mentee: Aadya Chinubhai (@aadya940)
GSoC 2024 project
https://summerofcode.withgoogle.com/programs/2024/projects/Hvd0DfkD
https://medium.com/@aadyachinubhai
Project Summary¶
Time series clustering involves grouping similar time series data together based on specific features or patterns. Deep learning algorithms have become increasingly popular for clustering. However, the aeon’s deep clustering module currently lacks several deep learning-based algorithms. In this project the aim is to implement some of the top performing and interesting algorithms from a recent comparison of deep learning for time series clustering and benchmark them. This project includes further developing the aeon deep learning networks module, making the package publicly documented for user to explore and well tested to help the maintenance of the deep learning implemented in the future.
Implement the Proximity Forest Algorithm for Time Series Classification¶
Mentors: Matthew Middlehurst (@MatthewMiddlehurst) and Tony Bagnall (@TonyBagnall) and Antoine Guillaume (@baraline)
Mentee: Divya Tiwari (@itsdivya1309)
https://summerofcode.withgoogle.com/programs/2024/projects/8TYGhJjy
https://medium.com/@Divya2003/
Project Summary¶
This project will implement and benchmark the Proximity Forest Algorithm for Time Series Classification in aeon. With the ever-increasing data, the applications of time series classification are also increasing. Hence, we need classification algorithms that are both efficient and scalable. The Proximity Forest Algorithm is the current state-of-the-art distance-based classifier that creates an ensemble of decision trees, where the splits are based on the similarity between time series measured using various parameterised distance measures. Currently, a version of Proximity Forest which can match the performance of the original implementation has not been implemented in Python. This project aims to implement Proximity Forest in aeon for the classification of univariate time series datasets of equal length and make it accessible for a greater variety of users. The implementation will be benchmarked on the UCR archive to match the results of the original Java implementation in terms of run time and accuracy.
Machine learning from EEG with aeon-neuro¶
Mentors: Tony Bagnall (@TonyBagnall) and Matthew Middlehurst (@MatthewMiddlehurst) and Aiden Rushbrooke (@AidenRushbrooke)
Mentee: Gabriel Riegner (@griegner)
GSoC 2024 project
https://summerofcode.withgoogle.com/programs/2024/projects/htrPCGOM
https://gist.github.com/griegner/c414f77d957dea73b84dcd80d580b602
Project Summary¶
Develop aeon-neuro to provide structured tools for machine learning from neural data. This project will focus on implementing algorithms for EEG classification by building on the multivariate classification algorithms outlined in Rushbrooke 2023. This paper demonstrates that existing time series models implemented in aeon can successfully classify patients from healthy individuals using frequency domain features alone, eliminating the need for detailed time domain feature selection. In addition to applying existing machine learning models to EEG datasets, we will further develop aeon-neuro to be more accessible to the scientific research community by interfacing it with existing data formatting standards (BIDs) and EEG analysis libraries (MNE). Alongside these primary outcomes, we will adhere to best practices in research software development, including writing well-test code, consistent documentation, and user-facing examples/notebooks.