| 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 | 15 | 2017 |
| 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 | 14 | 2019 |
| 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 Analytics …, 2017 | 11 | 2017 |
| 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 | 8 | 2021 |
| 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 | 8 | 2020 |
| Does automl outperform naive forecasting? GM Paldino, J De Stefani, F De Caro, G Bontempi Engineering proceedings 5 (1), 36, 2021 | 7 | 2021 |
| Factor-based framework for multivariate and multi-step-ahead forecasting of large scale time series J De Stefani, G Bontempi Frontiers in big Data, 75, 2021 | 5 | 2021 |
| 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 | 4 | 2022 |
| 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 ECML PKDD 2018 Workshops: MIDAS 2018 and PAP 2018, Dublin, Ireland …, 2019 | 4 | 2019 |
| System and Method for Managing Risks in a Process J De Stefani, G Bontempi, O Caelen, D Hattab | 1 | 2019 |
| Towards multivariate multi-step-ahead time series forecasting: A machine learning perspective J De Stefani Université libre de Bruxelles, 2022 | | 2022 |
| 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 Italy, 2015 | | 2015 |
| 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, ... | | |
Gianluca BontempiUniversité Libre de BruxellesVerified email at ulb.ac.be
