  | Bosser, T., & Ben taieb, S. (2023). Revisiting the Mark Conditional Independence Assumption in Neural Marked Temporal Point Processes. In Proceedings of the 31st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Louvain-La-Neuve, Belgium: i6doc. doi:10.14428/esann/2023.es2023-64  Peer reviewed |
 | Bosser, T., & Ben taieb, S. (2023). On the Predictive Accuracy of Neural Temporal Point Process Models for Continuous-time Event Data. Transactions on Machine Learning Research. Peer Reviewed verified by ORBi |
 | Dheur, V., & Ben taieb, S. (2023). A Large-Scale Study of Probabilistic Calibration in Neural Network Regression. In The 40th International Conference on Machine Learning. PMLR. Peer reviewed |
 | Meng, X., Taylor, J. W., Ben taieb, S., & Li, S. (2023). Scores for Multivariate Distributions and Level Sets. Operations Research. Peer Reviewed verified by ORBi |
 | Ben Taieb, S., & Taylor, K. S. (April 2022). Commentary on “Transparent modelling of influenza incidence”: On big data models for infectious disease forecasting. International Journal of Forecasting, 38 (2), 625-627. doi:10.1016/j.ijforecast.2021.02.003 Peer Reviewed verified by ORBi |
 | Petropoulos, F., Apiletti, D., Assimakopoulos, V., Babai, M. Z., Barrow, D. K., Ben Taieb, S., Bergmeir, C., Bessa, R. J., Bijak, J., Boylan, J. E., Browell, J., Carnevale, C., Castle, J. L., Cirillo, P., Clements, M. P., Cordeiro, C., Cyrino Oliveira, F. L., De Baets, S., Dokumentov, A., ... Ben Taieb, S. (2022). Forecasting: theory and practice. International Journal of Forecasting. doi:10.1016/j.ijforecast.2021.11.001 Peer Reviewed verified by ORBi |
 | Ben Taieb, S. (2022). Learning Quantile Functions for Temporal Point Processes with Recurrent Neural Splines. In The 25 th International Conference on Artificial Intelligence and Statistics (AISTATS) 2022. PMLR. Peer reviewed |
 | Roach, C., Hyndman, R., & Ben Taieb, S. (03 February 2021). Non‐linear mixed‐effects models for time series forecasting of smart meter demand. Journal of Forecasting, 40 (6), 1118-1130. doi:10.1002/for.2750 Peer Reviewed verified by ORBi |
 | Di Modica, C., Pinson, P., & Ben Taieb, S. (2021). Online forecast reconciliation in wind power prediction. Electric Power Systems Research. Peer Reviewed verified by ORBi |
 | Ben Taieb, S., Taylor, J. W., & Hyndman, R. J. (28 February 2020). Hierarchical Probabilistic Forecasting of Electricity Demand with Smart Meter Data. Journal of the American Statistical Association, 0 (0). Peer Reviewed verified by ORBi |
  | Vicendese, D., Te Marvelde, L., D. McNair, P., Whitfield, K., R. English, D., Ben Taieb, S., Hyndman, R. J., & Thomas, R. (2019). Hospital characteristics, rather than surgical volume, predict length of stay following colorectal cancer surgery. Australian and New Zealand Journal of Public Health. Peer Reviewed verified by ORBi |
 | Ben Taieb, S., & Koo, B. (2019). Regularized regression for hierarchical forecasting without unbiasdness conditions. In KDD '19: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, Unknown/unspecified: Association for Computing Machinery. doi:10.1145/3292500.3330976 Peer reviewed |
Ben taieb, S., Yu, J., Barreto, M., & Rajagopal, R. (2017). Regularization in Hierarchical Time Series Forecasting with Application to Electricity Smart Meter Data. In Proceedings of the AAAI Conference on Artificial Intelligence. AAAI. doi:10.1609/aaai.v31i1.11167 Peer reviewed |
Ben taieb, S. (2017). Sparse and Smooth Adjustments for Coherent Forecasts in Temporal Aggregation of Time Series. In Proceedings of the Time Series Workshop at NIPS 2016. PMLR. Peer reviewed |
Ben taieb, S., Taylor, J. W., & Hyndman, R. J. (2017). Coherent Probabilistic Forecasts for Hierarchical Time Series. In Proceedings of the 34th International Conference on Machine Learning. PMLR. Peer reviewed |
 | Ben Taieb, S., Huser, R., Hyndman, R. J., & Genton, M. G. (September 2016). Forecasting Uncertainty in Electricity Smart Meter Data by Boosting Additive Quantile Regression. IEEE Transactions on Smart Grid, 7 (5), 2448-2455. doi:10.1109/tsg.2016.2527820 Peer Reviewed verified by ORBi |
 | Ben Taieb, S., & Atiya, A. F. (January 2016). A Bias and Variance Analysis for Multistep-Ahead Time Series Forecasting. IEEE Transactions on Neural Networks and Learning Systems, 27 (1), 62-76. doi:10.1109/TNNLS.2015.2411629 Peer Reviewed verified by ORBi |
Dehwah, A. H., Ben taieb, S., Shamma, J. S., & Claudel, C. G. (2015). Decentralized energy and power estimation in solar-powered wireless sensor networks. In Proceedings - IEEE International Conference on Distributed Computing in Sensor Systems, DCOSS 2015. Institute of Electrical and Electronics Engineers Inc. doi:10.1109/DCOSS.2015.18 Peer reviewed |
 | Ben Taieb, S., & Hyndman, R. J. (April 2014). A gradient boosting approach to the Kaggle load forecasting competition. International Journal of Forecasting, 30 (2), 382-394. doi:10.1016/j.ijforecast.2013.07.005 Peer Reviewed verified by ORBi |
Ben taieb, S., & Hyndman, R. (2014). Boosting multi-step autoregressive forecasts. In Proceedings of the 31st International Conference on Machine Learning. PMLR. Peer reviewed |
 | Lerman, L., Bontempi, G., Ben Taieb, S., & Markowitch, O. (2013). A Time Series Approach for Profiling Attack. In Security, Privacy, and Applied Cryptography Engineering. Springer Berlin Heidelberg. doi:10.1007/978-3-642-41224-0_7 Peer reviewed |
Bontempi, G., Ben taieb, S., & Le Borgne, Y.-A. (2013). Machine learning strategies for time series forecasting. In Business Intelligence - Second European Summer School, eBISS 2012, Tutorial Lectures. Springer Verlag. doi:10.1007/978-3-642-36318-4_3 Peer reviewed |
 | Ben taieb, S., Bontempi, G., Atiya, A. F., & Sorjamaa, A. (15 June 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. doi:10.1016/j.eswa.2012.01.039 Peer Reviewed verified by ORBi |
 | Vaccaro, A., Bontempi, G., Ben taieb, S., & Villacci, D. (February 2012). Adaptive local learning techniques for multiple-step-ahead wind speed forecasting. Electric Power Systems Research, 83 (1), 129 - 135. doi:10.1016/j.epsr.2011.10.008 Peer Reviewed verified by ORBi |
Ben taieb, S., & Bontempi, G. (2011). Recursive multi-step time series forecasting by perturbing data. In Proceedings - 11th IEEE International Conference on Data Mining, ICDM 2011. IEEE. doi:10.1109/ICDM.2011.123 Peer reviewed |
 | Bontempi, G., & Ben taieb, S. (July 2011). Conditionally dependent strategies for multiple-step-ahead prediction in local learning. International Journal of Forecasting, 27 (3), 689 - 699. doi:10.1016/j.ijforecast.2010.09.004 Peer Reviewed verified by ORBi |
 | Ben taieb, S., Sorjamaa, A., & Bontempi, G. (June 2010). Multiple-output modeling for multi-step-ahead time series forecasting. Neurocomputing, 73 (10-12), 1950 - 1957. doi:10.1016/j.neucom.2009.11.030 Peer Reviewed verified by ORBi |
Ben taieb, S., Bontempi, G., Sorjamaa, A., & Lendasse, A. (2009). Long-term prediction of time series by combining direct and MIMO strategies. In 2009 International Joint Conference on Neural Networks, IJCNN 2009. IEEE. doi:10.1109/IJCNN.2009.5178802 Peer reviewed |
