Unpublished working papers
2024
- Tanguy Bosser, Souhaib Ben Taieb (2024) A Unifying Framework for Independent Training of Time and Mark Predictive Distributions in Neural Marked Temporal Point Processes. Abstract
- Sukanya Patra, Nicolas Sournac, Souhaib Ben Taieb (2024) Detecting Abnormal Operations in Concentrated Solar Power Plants from Irregular Sequences of Thermal Images. Abstract
- Victor Dheur, Tanguy Bosser, Souhaib Ben Taieb (2024) Distribution-Free Conformal Joint Prediction Regions for Neural Marked Temporal Point Processes. Submitted to the Machine Learning Journal. Abstract Arxiv
Published articles
2024
- Le Thi Khanh Hien, Sukanya Patra, Souhaib Ben Taieb (2024) Anomaly detection with semi-supervised classification based on risk estimators. Transactions on Machine Learning Research. Abstract Arxiv
- Victor Dheur, Souhaib Ben Taieb (2024) Probabilistic Calibration by Design for Neural Network Regression. Proceedings of the 27th International Conference on Artificial Intelligence and Statistics (AISTATS) 2024.. Abstract
2023
- Xiaochun Meng, James W. Taylor, Souhaib Ben Taieb, Siran Li (2023) Scores for Multivariate Distributions and Level Sets. Operations Research (To appear). Abstract Arxiv
- Souhaib Ben Taieb, Tanguy Bosser (2023) On the Predictive Accuracy of Neural Temporal Point Process Models for Continuous-time Event Data. Transactions on Machine Learning Research(To appear). Abstract Arxiv
- Victor Dheur, Souhaib Ben Taieb (2023) A Large-Scale Study of Probabilistic Calibration in Neural Network Regression. Proceedings of the 40th International Conference on Machine Learning, PMLR 202, 2023.. Abstract Arxiv
2022
- Souhaib Ben Taieb (2022) Learning Quantile Functions for Temporal Point Processes with Recurrent Neural Splines. Proceedings of the 25 th International Conference on Artificial Intelligence and Statistics (AISTATS) 2022, Valencia, Spain. PMLR, Volume 151.. Abstract pdf
- Fotios Petropoulos, et al. (2022) Forecasting: theory and practice. International Journal of Forecasting.. Abstract Arxiv
- Souhaib Ben Taieb and Kathryn S. Taylor (2022) Commentary on “Transparent modelling of influenza incidence”: On big data models for infectious disease forecasting. Abstract Online
2021
- Cameron Roach, Rob J Hyndman, Souhaib Ben Taieb (2021) Nonlinear mixed effects models for time series forecasting of smart meter demand. Journal of Forecasting 40(6), 1118-1130. Abstract pdf
- Chiara Di , Modica Pierre Pinson, Souhaib Ben Taieb (2021) Online forecast reconciliation in wind power prediction. Electric Power Systems Research, Volume 190, 2021. Abstract DOI
2020
2019
- D Vicendese, L Te Marvelde, PD McNair, K Whitfield, DR English, S Ben Taieb, RJ Hyndman, R Thomas (2019) Hospital characteristics, rather than surgical volume, predict length of stay following colorectal cancer surgery. Australian and New Zealand Journal of Public Health, 44: 73-82. Abstract DOI
- Souhaib Ben Taieb and Bonsoo Koo (2019) Regularized regression for hierarchical forecasting without unbiasdness conditions. Proceedings of Knowledge Discovery and Data Mining (KDD), 2019.. Abstract Online pdf
2017
- Souhaib Ben Taieb, James W Taylor, Rob J Hyndman (2017) Coherent Probabilistic Forecasts for Hierarchical Time Series. Proceedings of the 34th International Conference on Machine Learning, PMLR 70:3348-3357. Abstract Online pdf
- Souhaib Ben Taieb, Jiafan Yu, Mateus Neves Barreto and Ram Rajagopal (2017) Regularization in Hierarchical Time Series Forecasting With Application to Electricity Smart Meter Data. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence. AAAI Press, 2017.. Abstract Online pdf
- Souhaib Ben Taieb (2017) Sparse and Smooth Adjustments for Coherent Forecasts in Temporal Aggregation of Time Series. NIPS 2016 Time Series Workshop, JMLR W&CP Vol. 55, 2017.. Abstract Online pdf
2016
- Souhaib Ben Taieb, Raphael Huser, Rob J Hyndman and Marc G Genton (2016) Forecasting uncertainty in electricity smart meter data by boosting additive quantile regression. IEEE Transactions on Smart Grid 7(5), 2448-2455. Abstract DOI pdf
- Souhaib Ben Taieb and Amir F. Atiya (2016) A Bias and Variance Analysis for Multistep-Ahead Time Series Forecasting. IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 1, pp. 62-76, 2016. Abstract Online pdf
2014
- Souhaib Ben Taieb (2014) Machine learning strategies for multi-step-ahead time series forecasting. PhD thesis, Free University of Brussels. Abstract pdf
- Souhaib Ben Taieb, Rob J Hyndman (2014) A gradient boosting approach to the Kaggle load forecasting competition. International Journal of Forecasting 30(2), 382–394. Abstract DOI pdf
- Souhaib Ben Taieb, Rob J Hyndman (2014) Boosting multi-step autoregressive forecasts. Proceedings of the International Conference on Machine Learning (ICML), Beijing, China. Abstract pdf
2013
- Liran Lerman, Gianluca Bontempi, Souhaib Ben Taieb and Olivier Markowitch (2013) A Time Series Approach for Profiling Attack. Lecture Notes in Computer Science, Volume 8204, 75-94, 2013. Abstract Online
- Gianluca Bontempi, Souhaib Ben Taieb and Y.-A. Le Borgne (2013) Machine Learning Strategies for Time Series Forecasting. Business Intelligence, Vol. 138. Springer, pp.62–77, 2013.. Abstract Online
2012
- Souhaib Ben Taieb, Gianluca Bontempi, Amir F. Atiya and Antti Sorjamaa (2012) A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition. Expert Systems with Applications, 39(8):7067–7083, 2012.. Abstract Online
- Alfredo Vaccaro, Gianluca Bontempi, Souhaib Ben Taieb and Domenico Villacci (2012) Adaptive local learning techniques for multiple-step-ahead wind speed forecasting. Electric Power Systems Research, 83(1):129–135, 2012.. Abstract Online
2011
- Souhaib Ben Taieb and Gianluca Bontempi (2011) Recursive Multi-step Time Series Forecasting by Perturbing Data. IEEE International Conference on Data Mining (ICDM), December, 2011.. Abstract Online
- Alfredo Vaccaro, Gianluca Bontempi, Souhaib Ben Taieb and Domenico Villacci (2011) Conditionally dependent strategies for multiple-step-ahead prediction in local learning. International Journal of Forecasting, 27(3):689–699, 2011.. Abstract Online