Page Not Found
Page not found. Your pixels are in another canvas.
Sitemap
A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
About me
About me
Page not in menu
This is a page not in th emain menu
Posts
Predicting Tomorrow: A Beginner’s Guide to AR and MA in Time Series
Published:
Time Series
Recommended citation: https://medium.com/@youssefachenchabe/predicting-tomorrow-a-beginners-guide-to-ar-and-ma-in-time-series-9e3c292685f6
From Chaos to Clarity: Understanding Time Series Decomposition
Published:
Time Series
Recommended citation: https://medium.com/@youssefachenchabe/from-chaos-to-clarity-understanding-time-series-decomposition-2e35f28b5b96
Unlocking Time Series Insights: Why Stationarity and Autocorrelation Matter?
Published:
Time Series
Recommended citation: https://medium.com/@youssefachenchabe/unlocking-time-series-insights-why-stationarity-and-autocorrelation-matter-4cb4e45769ab?source=friends_link&sk=2eeaec5698184a5380b510277c66d6dc
What’s special about time series data?
Published:
Time Series
Recommended citation: https://medium.com/p/421458bd173e
portfolio
ML-EDM
ml_edm package: a Python toolkit for Machine Learning based Early Decision Making
Pymlfs
New open source python package that implements classic machine learning models from scratch for educational purposes.
publications
Early classification of time series: Cost-based optimization criterion and algorithms
Published in Machine Learning, 2021
Recommended citation: Achenchabe, Y., Bondu, A., Cornuéjols, A., & Dachraoui, A. (2021). Early classification of time series: Cost-based optimization criterion and algorithms. Machine Learning, 110(6), 1481-1504. https://link.springer.com/article/10.1007/s10994-021-05974-z
Early and Revocable Time Series Classification
Published in IEEE WCCI-IJCNN, International Joint Conference on Neural Networks, 2021
Recommended citation: Achenchabe, Y., Bondu, A., Cornuéjols, A., & Lemaire, V. (2022, July). Early and revocable time series classification. In 2022 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE. https://ieeexplore.ieee.org/document/9892391
Early classification of time series: Cost-based multiclass algorithms
Published in IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2021
Recommended citation: Zafar, P. E., Achenchabe, Y., Bondu, A., Cornuéjols, A., & Lemaire, V. (2021, October). Early classification of time series: Cost-based multiclass algorithms. In 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA) (pp. 1-10). IEEE. https://ieeexplore.ieee.org/abstract/document/9564134
When to Classify Events in Open Times Series?
Published in In Asian Conference on Machine Learning, 2022
Recommended citation: Achenchabe, Y., Bondu, A., Antoine, C., & Vincent, L. (2023, April). When to Classify Events in Open Times Series?. In Asian Conference on Machine Learning (pp. 1-16). PMLR. https://proceedings.mlr.press/v189/achenchabe23a/achenchabe23a.pdf
From the early classification of time series to machine learning-based early decision-making
Published in Theses, 2022
Recommended citation: Achenchabe, Y. (2022). From the early classification of time series to machine learning-based early decision-making (Doctoral dissertation, Université Paris-Saclay). https://shorturl.at/dtHZ0
Open challenges for Machine Learning based Early Decision-Making research
Published in ACM SIGKDD, 2022
Recommended citation: Bondu, A., Achenchabe, Y., Bifet, A., Clérot, F., Cornuéjols, A., Gama, J., ... & Marteau, P. F. (2022). Open challenges for machine learning based early decision-making research. ACM SIGKDD Explorations Newsletter, 24(2), 12-31. https://dl.acm.org/doi/abs/10.1145/3575637.3575643
SANGEA: Scalable and Attributed Network Generation
Published in In Asian Conference on Machine Learning, 2023
Recommended citation: Lemaire, V., Achenchabe, Y., Ody, L., Souid, H. E., Aversano, G., Posocco, N., & Skhiri, S. (2024, February). SANGEA: Scalable and Attributed Network Generation. In Asian Conference on Machine Learning (pp. 678-693). PMLR. https://proceedings.mlr.press/v222/lemaire24a/lemaire24a.pdf
Temporal Graph Generative Models: An empirical study
Published in EuroMLSys 24: Proceedings of the 4th Workshop on Machine Learning and Systems, 2024
Recommended citation: Souid, H. E., Ody, L., Achenchabe, Y., Lemaire, V., Aversano, G., & Skhiri, S. (2024, April). Temporal Graph Generative Models: An empirical study. In Proceedings of the 4th Workshop on Machine Learning and Systems (pp. 18-27). https://dl.acm.org/doi/abs/10.1145/3642970.3655829
ml_edm package: a Python toolkit for Machine Learning based Early Decision Making
Published in Preprint, 2024
Recommended citation: Renault, A., Achenchabe, Y., Bertrand, É., Bondu, A., Cornuéjols, A., Lemaire, V., & Dachraoui, A. (2024). ml_edm package: a Python toolkit for Machine Learning based Early Decision Making. arXiv preprint arXiv:2408.12925. https://arxiv.org/pdf/2408.12925
talks
Talk 1 on Relevant Topic in Your Field
Published:
This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
Published:
This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.