| Machine learning strategies for time series forecasting G Bontempi, S Ben Taieb, YA Le Borgne Business Intelligence, 62-77, 2013 | 787 | 2013 |
| A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition S Ben Taieb, G Bontempi, AF Atiya, A Sorjamaa Expert systems with applications 39 (8), 7067-7083, 2012 | 735 | 2012 |
| Forecasting: theory and practice F Petropoulos, D Apiletti, V Assimakopoulos, MZ Babai, DK Barrow, ... International Journal of Forecasting 38 (3), 705-871, 2022 | 635 | 2022 |
| A gradient boosting approach to the Kaggle load forecasting competition S Ben Taieb, RJ Hyndman International journal of forecasting 30 (2), 382-394, 2014 | 347 | 2014 |
| Multiple-output modeling for multi-step-ahead time series forecasting S Ben Taieb, A Sorjamaa, G Bontempi Neurocomputing 73 (10), 1950-1957, 2010 | 277 | 2010 |
| Forecasting uncertainty in electricity smart meter data by boosting additive quantile regression SB Taieb, R Huser, RJ Hyndman, MG Genton IEEE Transactions on Smart Grid 7 (5), 2448-2455, 2016 | 223 | 2016 |
| A Bias and Variance Analysis for Multistep-Ahead Time Series Forecasting S Ben Taieb, AF Atiya IEEE Transactions on Neural Networks and Learning Systems 27 (1), 62-76, 2015 | 191 | 2015 |
| Hierarchical probabilistic forecasting of electricity demand with smart meter data SB Taieb, JW Taylor, RJ Hyndman Journal of the American Statistical Association 116 (533), 27-43, 2021 | 141 | 2021 |
| Long-term prediction of time series by combining direct and mimo strategies SB Taieb, G Bontempi, A Sorjamaa, A Lendasse 2009 International Joint Conference on Neural Networks, 3054-3061, 2009 | 116 | 2009 |
| Recursive and direct multi-step forecasting: the best of both worlds S Ben Taieb, RJ Hyndman Wroking paper, 2012 | 107* | 2012 |
| Coherent probabilistic forecasts for hierarchical time series SB Taieb, JW Taylor, RJ Hyndman International Conference on Machine Learning, 3348-3357, 2017 | 106 | 2017 |
| Conditionally dependent strategies for multiple-step-ahead prediction in local learning G Bontempi, S Ben Taieb International journal of forecasting 27 (3), 689-699, 2011 | 97 | 2011 |
| Regularized regression for hierarchical forecasting without unbiasedness conditions S Ben Taieb, B Koo Proceedings of the 25th ACM SIGKDD international conference on knowledge …, 2019 | 58 | 2019 |
| Boosting multi-step autoregressive forecasts SB Taieb, R Hyndman International conference on machine learning, 109-117, 2014 | 55 | 2014 |
| Machine learning strategies for multi-step-ahead time series forecasting S Ben Taieb Université Libre de Bruxelles, 2014 | 46* | 2014 |
| Regularization in hierarchical time series forecasting with application to electricity smart meter data SB Taieb, J Yu, M Barreto, R Rajagopal Proceedings of the AAAI Conference on Artificial Intelligence 31 (1), 2017 | 40 | 2017 |
| A time series approach for profiling attack L Lerman, G Bontempi, S Ben Taieb, O Markowitch Security, Privacy, and Applied Cryptography Engineering, 75-94, 2013 | 28 | 2013 |
| Adaptive local learning techniques for multiple-step-ahead wind speed forecasting A Vaccaro, G Bontempi, S Ben Taieb, D Villacci Electric power systems research 83 (1), 129-135, 2012 | 28 | 2012 |
| Recursive multi-step time series forecasting by perturbing data SB Taieb, G Bontempi 2011 IEEE 11th International Conference on Data Mining, 695-704, 2011 | 28 | 2011 |
| Hospital characteristics, rather than surgical volume, predict length of stay following colorectal cancer surgery D Vicendese, L Te Marvelde, PD McNair, K Whitfield, DR English, ... Australian and New Zealand journal of public health 44 (1), 73-82, 2020 | 16 | 2020 |
Gianluca BontempiMachine Learning Group, Université Libre de Bruxelles在 ulb.ac.be 的电子邮件经过验证
