Papers using aeon¶
This is a list of papers that use aeon. If you have a paper that uses aeon,
please add it to this list by making a pull request. Please include a hyperlink to
the paper and a link to the code in your personal GitHub or other repository.
If you want to reference aeon please reference this paper.
Middlehurst, M., Ismail-Fawaz, A., Guillaume, A., Holder, C., Guijo-Rubio, D., Bulatova, G., Tsaprounis, L., Mentel, L., Walter, M., Schäfer, P. and Bagnall, A., 2024. aeon: a Python toolkit for learning from time series. Journal of Machine Learning Research, 25(289), pp.1-10. Paper
2025¶
Ismail-Fawaz, A., Devanne, M., Berretti, S., Weber, J. and Forestier, G., 2025. Establishing a unified evaluation framework for human motion generation: A comparative analysis of metrics. Computer Vision and Image Understanding, 254, p.104337. Paper Code
Rewicki, F,Denzler J. and Niebling, J., Anomalous Agreement: How to find the Ideal Number of Anomaly Classes in Correlated, Multivariate Time Series Data. arXiv:2501.07172 Paper Code
Serramazza, D., Nguyen, T. and Ifrim, G. A short tutorial for multivariate time series explanation using tsCaptum. Software Impacts, 22. Paper
2024¶
Bagnall, A.,Middlehurst, M., Forestier, G., Schäfer, P., Ismail-Fawaz, A., Guillaume, A., Guijo-Rubio, D., Wei Tan, C., Dempster A. and Webb, G.I. A hands-on introduction to time series classification and regression. Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining Paper Webpage/Code
Dempster, A., Tan, C.W., Miller, L., Foumani, N.M., Schmidt, D.F. and Webb, G.I. Highly Scalable Time Series Classification for Very Large Datasets. In International Workshop on Advanced Analytics and Learning on Temporal Data. pp. 80-95. Paper
Dempster, A., Schmidt, D.F. and Webb, G.I. Quant: A minimalist interval method for time series classification. Data Mining and Knowledge Discovery, 38(4), pp.2377-2402. Paper
Serramazza, D.I., Nguyen, T.L. and Ifrim, G. Improving the evaluation and actionability of explanation methods for multivariate time series classification. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 177-195. Paper
Middlehurst, M., Schäfer, P. and Bagnall, A. Bake off redux: a review and experimental evaluation of recent time series classification algorithms. Data Mining and Knowledge Discovery, 38(4), pp.1958-2031. Paper Webpage/Code
Spinnato, F., Guidotti, R., Monreale, A. and Nanni, M. Fast, Interpretable and Deterministic Time Series Classification with a Bag-Of-Receptive-Fields. IEEE Access. Paper Code
Holder, C., Middlehurst, M. and Bagnall, A. A review and evaluation of elastic distance functions for time series clustering. Knowledge and Information Systems, 66(2), pp.765-809. Paper Webpage/Code
Ayllón-Gavilán, R., Guijo-Rubio, D., Gutiérrez, P.A., Bagnall, A. and Hervás-Martínez, C. Convolutional-and Deep Learning-Based Techniques for Time Series Ordinal Classification. IEEE Transactions on Cybernetics, 55(2), pp. 537-549. Paper.
Guijo-Rubio, D., Middlehurst, M., Arcencio, G., Silva, D.F. and Bagnall, A.. Unsupervised feature based algorithms for time series extrinsic regression. Data Mining and Knowledge Discovery, 38(4), pp.2141-2185. Paper Webpage/Code
Miao, B., Dong, Y, Theissler, A, Lesh, A., Loftus, D. and Lepech, M. BioSys: Efficient Quality Control System for Manufacturing of Sustainable Biopolymer Composites. In Proceedings of the 11th ACM International Conference on Systems for Energy-Efficient Buildings (BuildSys ‘24) Paper
Moore, J., Stackhouse, H. Fulcher, B. and Mahmoodian, S. Using matrix-product states for time-series machine learning. arXiv:2412.15826 Paper
Holder, C. and Bagnall, B. 2024. Rock the KASBA: Blazingly Fast and Accurate Time Series Clustering. arXiv: 2411.17838 Paper
Holder, C., Bagnall, B. and Lines, J. 2024. On time series clustering with k-means. arXiv: 2410.14269 Paper
Ismail-Fawaz, A. 2024. Deep Learning For Time Series Analysis With Application On Human Motion arXiv: 2502.19364 Paper
2023¶
Ayllón-Gavilán, R., Guijo-Rubio, D., Gutiérrez, P.A. and Hervás-Martínez, C., 2023, June. A dictionary-based approach to time series ordinal classification. In International Work-Conference on Artificial Neural Networks (pp. 541-552). Cham: Springer Nature Switzerland. Paper
Ermshaus, A., Schäfer, P., Bagnall, A., Guyet, T., Ifrim, G., Lemaire, V., … & Malinowski, S. (2023, September). Human Activity Segmentation Challenge@ ECML/PKDD’23. In International Workshop on Advanced Analytics and Learning on Temporal Data (pp. 3-13). Cham: Springer Nature Switzerland. Paper Webpage/Code
Schäfer, P. and Leser, U., 2023. WEASEL 2.0: a random dilated dictionary transform for fast, accurate and memory constrained time series classification. Machine Learning, 112(12), pp.4763-4788. Paper Webpage/Code
Middlehurst, M. and Bagnall, A., (2023), September. Middlehurst, M. and Bagnall, A., 2023, September. Extracting features from random subseries: A hybrid pipeline for time series classification and extrinsic regression. In International Workshop on Advanced Analytics and Learning on Temporal Data (pp. 113-126). Cham: Springer Nature Switzerland. Paper Webpage/Code
Ismail-Fawaz, A., Ismail Fawaz, H., Petitjean, F., Devanne, M., Weber, J., Berretti, S., Webb, G.I. and Forestier, G., 2023, September. Shapedba: Generating effective time series prototypes using shapedtw barycenter averaging. In International Workshop on Advanced Analytics and Learning on Temporal Data (pp. 127-142). Cham: Springer Nature Switzerland. Paper Code
Holder, C., Guijo-Rubio, D., & Bagnall, A. J. (2023). Barycentre Averaging for the Move-Split-Merge Time Series Distance Measure. In Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (pp. 51-62). Paper
Holder, C., Guijo-Rubio, D. and Bagnall, A., 2023, September. Clustering time series with k-medoids based algorithms. In International Workshop on Advanced Analytics and Learning on Temporal Data (pp. 39-55). Cham: Springer Nature Switzerland. Paper