[DOCS] Add any papers

docs/papers_using_aeon.md

@@ -1,34 +1,53 @@
 # 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 sending a pull request. Please include a hyperlink to
+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.
 
 ## Classification
 
-- Middlehurst, M. and Schäfer, P. and  Bagnall, A. (2023). Bake off redux: a review
-  and experimental evaluation of recent time series classification algorithms. ArXiv.
-  [Paper](https://arxiv.org/abs/2304.13029) [Webpage/Code](https://tsml-eval.readthedocs.io/en/stable/publications/2023/tsc_bakeoff/tsc_bakeoff_2023.html)
+- Middlehurst, M. and 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, online first, open access.
+  [Paper](https://link.springer.com/article/10.1007/s10618-024-01022-1) [Webpage/Code](https://tsml-eval.readthedocs.io/en/stable/publications/2023/tsc_bakeoff/tsc_bakeoff_2023.html)
 
 ## Clustering
 
-- 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.
+- 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.
   [Paper](https://link.springer.com/article/10.1007/s10115-023-01952-0) [Webpage/Code](https://tsml-eval.readthedocs.io/en/stable/publications/2023/distance_based_clustering/distance_based_clustering.html)
-
 - 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).
   [Paper](https://link.springer.com/chapter/10.1007/978-3-031-49896-1_4)
 
 ## Regression
 
-- Guijo-Rubio, D., Middlehurst, M., Arcencio, G., Silva, D. and Bagnall, A. (2023).
-  Unsupervised Feature Based Algorithms for Time Series Extrinsic Regression. ArXiv.
+- Guijo-Rubio, D., Middlehurst, M., Arcencio, G., Silva, D. and Bagnall, A. (2024).
+  Unsupervised Feature Based Algorithms for Time Series Extrinsic Regression. Data
+  Mining and Knowledge Discovery, online first, open access.
   [Paper](https://arxiv.org/abs/2305.01429) [Webpage/Code](https://tsml-eval.readthedocs.io/en/stable/publications/2023/tser_archive_expansion/tser_archive_expansion.html)
-- Middlehurst, M. and Bagnall, A., 2023, September. Extracting Features from Random
+- 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).
   [Paper](https://link.springer.com/chapter/10.1007/978-3-031-49896-1_8) [Webpage/Code](https://tsml-eval.readthedocs.io/en/stable/publications/2023/rist_pipeline/rist_pipeline.html)
+
+## Ordinal classification
+
+- 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. [ArXiV](https://arxiv.org/abs/2306.10084).
+- Ayllón-Gavilán, R., Guijo-Rubio, D., Gutiérrez, P. A., and Hervás-Martínez, C. (2024). O-Hydra: A Hybrid Convolutional and Dictionary-Based Approach to Time Series Ordinal Classification. In Conference of the Spanish Association for Artificial Intelligence (pp. 50-60). [Paper](https://link.springer.com/chapter/10.1007/978-3-031-62799-6_6).
+- Ayllón-Gavilán, R., Guijo-Rubio, D., Gutiérrez, P.A., and Hervás-Martínez, C. (2023). A Dictionary-Based Approach to Time Series Ordinal Classification. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2023. Lecture Notes in Computer Science, vol 14135. [Paper](https://link.springer.com/chapter/10.1007/978-3-031-43078-7_44).
+
+## Prototyping
+
+- Ismail-Fawaz, A. and Ismail Fawaz, H. and Petitjean, F. and Devanne, M. and Weber,
+  J. and Berretti, S. and Webb, GI. and Forestier, G. (2023 December "ShapeDBA: Generating Effective Time Series Prototypes Using ShapeDTW Barycenter Averaging." ECML/PKDD Workshop on Advanced Analytics and Learning on Temporal Data. [Paper](https://doi.org/10.1007/978-3-031-49896-1_9) [code](https://github.com/MSD-IRIMAS/ShapeDBA)
+- 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-Volume 1:, 51-62, pp. 51-62. [Paper](https://www.scitepress.org/Link.aspx?doi=10.5220/0012164900003598)
+
+## Generation Evaluation
+
+- Ismail-Fawaz, A. and Devanne, M. and Berretti, S. and Weber, J. and Forestier, G.
+  (2024) May "Establishing a Unified Evaluation Framework for Human Motion
+  Generation: A Comparative Analysis of Metrics" [Paper](https://arxiv.org/abs/2405.07680) [code](https://github.com/MSD-IRIMAS/Evaluating-HMG)