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Jacopo De Stefani
Jacopo De Stefani
Expert Data Scientist @ Euroconsumers | Scientific collaborator @ UniversitÚ Libre de Bruxelles
Verified email at ulb.ac.be - Homepage
Title
Cited by
Cited by
Year
Batch and incremental dynamic factor machine learning for multivariate and multi-step-ahead forecasting
J De Stefani, YA Le Borgne, O Caelen, D Hattab, G Bontempi
International Journal of Data Science and Analytics 7 (4), 311-329, 2019
232019
DAFT-E: feature-based multivariate and multi-step-ahead wind power forecasting
F De Caro, J De Stefani, A Vaccaro, G Bontempi
IEEE Transactions on sustainable energy 13 (2), 1199-1209, 2021
222021
Does automl outperform naive forecasting?
GM Paldino, J De Stefani, F De Caro, G Bontempi
Engineering proceedings 5 (1), 36, 2021
192021
Machine Learning for Multi-step Ahead Forecasting of Volatility Proxies.
J De Stefani, O Caelen, D Hattab, G Bontempi
MIDAS@ PKDD/ECML, 17-28, 2017
152017
A digital twin approach for improving estimation accuracy in dynamic thermal rating of transmission lines
GM Paldino, F De Caro, J De Stefani, A Vaccaro, D Villacci, G Bontempi
Energies 15 (6), 2254, 2022
132022
Robust assessment of short-term wind power forecasting models on multiple time horizons
F De Caro, J De Stefani, G Bontempi, A Vaccaro, D Villacci
Technology and Economics of Smart Grids and Sustainable Energy 5, 1-15, 2020
132020
A dynamic factor machine learning method for multi-variate and multi-step-ahead forecasting
G Bontempi, YA Le Borgne, J De Stefani
2017 IEEE International Conference on Data Science and Advanced Analyticsá…, 2017
112017
Factor-based framework for multivariate and multi-step-ahead forecasting of large scale time series
J De Stefani, G Bontempi
Frontiers in big Data 4, 690267, 2021
82021
A multivariate and multi-step ahead machine learning approach to traditional and cryptocurrencies volatility forecasting
J De Stefani, O Caelen, D Hattab, YA Le Borgne, G Bontempi
Workshop on Mining Data for Financial Applications, 7-22, 2018
52018
Towards multivariate multi-step-ahead time series forecasting: A machine learning perspective
J De Stefani
UniversitÚ libre de Bruxelles, 2022
22022
A study of deep active learning methods to reduce labelling efforts in biomedical relation extraction
C Nachtegael, J De Stefani, T Lenaerts
PloS one 18 (12), e0292356, 2023
12023
ALAMBIC: Active Learning Automation Methods to Battle Inefficient Curation
C Nachtegael, J De Stefani, T Lenaerts
Proceedings of the 17th Conference of the European Chapter of theá…, 2023
12023
System and Method for Managing Risks in a Process
J De Stefani, G Bontempi, O Caelen, D Hattab
12019
Transfer learning-based methodologies for Dynamic Thermal Rating of transmission lines
GM Paldino, F De Caro, J De Stefani, A Vaccaro, G Bontempi
Electric Power Systems Research 229, 110206, 2024
2024
DUVEL: an active-learning annotated biomedical corpus for the recognition of oligogenic combinations
C Nachtegael, J De Stefani, A Cnudde, T Lenaerts
Database 2024, baae039, 2024
2024
Multi-step-ahead prediction of volatility proxies
J De Stefani, G Bontempi, O Caelen, D Hattab
Benelearn 2017 1 (Proceedings), 105-107, 2017
2017
Spatial allocation in swarm robotics
J De Stefani
Politecnico di Milano, 2013
2013
Everything you always wanted to know about ML and videogames (but were afraid to ask)
J De Stefani
SPECIAL SECTION ON ADVANCES IN RENEWABLE ENERGY FORECASTING: PREDICTABILITY, BUSINESS MODELS AND APPLICATIONS IN THE POWER INDUSTRY
RJ Bessa, P Pinson, G Kariniotakis, D Srinivasan, C Smith, N Amjady, ...
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