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Research Output 2010 2019

  • 7 Conference Paper
  • 6 Article
  • 1 Chapter (Book)
  • 1 Review Article
2019

Regularized regression for hierarchical forecasting without unbiasedness conditions

Taieb, S. B. & Koo, B., 2019, KDD 2019: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. Li, Y., Rosales, R., Terzi, E. & Karypis, G. (eds.). New York NY USA: Association for Computing Machinery (ACM), p. 1337-1347 11 p.

Research output: Chapter in Book/Report/Conference proceedingConference PaperOtherpeer-review

2017

Coherent probabilistic forecasts for hierarchical time series

Taieb, S. B., Taylor, J. W. & Hyndman, R. J., 1 Jan 2017, Proceedings of the 34th International Conference on Machine Learning. Precup, D. & Teh, Y. W. (eds.). Massachusetts USA: Proceedings of Machine Learning Research (PMLR), p. 3348-3357 10 p. (Proceedings of Machine Learning Research; vol. 70).

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Regularization in hierarchical time series forecasting with application to electricity smart meter data

Ben Taieb, S., Yu, J., Barreto, M. N. & Rajagopal, R., 2017, Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17). Singh, S. & Markovitch, S. (eds.). Palto Alto CA USA: Association for the Advancement of Artificial Intelligence (AAAI), p. 4474-4480 7 p.

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Open Access
2016

A bias and variance analysis for multistep-ahead time series forecasting

Ben Taieb, S. & Atiya, A. F., 1 Jan 2016, In : IEEE Transactions on Neural Networks and Learning Systems. 27, 1, p. 62-76 15 p., 7064712.

Research output: Contribution to journalArticleResearchpeer-review

Forecasting uncertainty in electricity smart meter data by boosting additive quantile regression

Ben Taieb, S., Huser, R., Hyndman, R. J. & Genton, M. G., 1 Sep 2016, In : IEEE Transactions on Smart Grid. 7, 5, p. 2448-2455 8 p., 7423794.

Research output: Contribution to journalArticleResearchpeer-review

Sparse and smooth adjustments for coherent forecasts in temporal aggregation of time series

Ben Taieb, S., 2016, NIPS 2016 Time Series Workshop, 09 December 2016, Barcelona, Spain. Anava, O., Khaleghi, A., Cuturi, M., Kuznetsov, V. & Rakhlin, A. (eds.). USA: Proceedings of Machine Learning Research (PMLR), Vol. 55. 11 p.

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Open Access
2014

A gradient boosting approach to the Kaggle load forecasting competition

Ben Taieb, S. & Hyndman, R. J., 2014, In : International Journal of Forecasting. 30, 2, p. 382 - 394 13 p.

Research output: Contribution to journalArticleResearchpeer-review

Boosting multi-step autoregressive forecasts

Ben Taieb, S. & Hyndman, R. J., 2014, Proceedings of the 31st International Conference on Machine Learning. Xing, E. P. & Jebara, T. (eds.). International Machine Learning Society (IMLS), Vol. 32. 9 p. (Proceedings of Machine Learning Research; vol. 32).

Research output: Chapter in Book/Report/Conference proceedingConference PaperOtherpeer-review

2013

A time series approach for profiling attack

Lerman, L., Bontempi, G., Ben Taieb, S. & Markowitch, O., 2013, Security, Privacy, and Applied Cryptography Engineering: Third International Conference, SPACE 2013 Kharagpur, India, October 19-23, 2013 Proceedings. Gierlichs, B., Guilley, S. & Mukhopadhyay, D. (eds.). Berlin Germany: Springer, p. 75-94 20 p. (Lecture Notes in Computer Science; vol. 8204).

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Machine learning strategies for time series forecasting

Bontempi, G., Ben Taieb, S. & Le borgne, Y-A., 2013, Business Intelligence: Second European Summer School, eBISS 2012, Brussels, Belgium, July 15-21, 2012, Tutorial Lectures. Aufaure, M-A. & Zimányi, E. (eds.). Berlin Germany: Springer, p. 62-77 16 p. (Lecture Notes in Business Information Processing; vol. 138).

Research output: Chapter in Book/Report/Conference proceedingChapter (Book)Researchpeer-review

2012

Adaptive local learning techniques for multiple-step-ahead wind speed forecasting

Vaccaro, A., Bontempi, G., Ben Taieb, S. & Villacci, D., Feb 2012, In : Electric Power Systems Research. 83, 1, p. 129-135 7 p.

Research output: Contribution to journalArticleResearchpeer-review

A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition

Ben Taieb, S., Bontempi, G., Atiya, A. F. & Sorjamaa, A., 15 Jun 2012, In : Expert Systems with Applications. 39, 8, p. 7067-7083 17 p.

Research output: Contribution to journalReview ArticleResearchpeer-review

2011

Conditionally dependent strategies for multiple-step-ahead prediction in local learning

Bontempi, G. & Ben Taieb, S., Jul 2011, In : International Journal of Forecasting. 27, 3, p. 689-699 11 p.

Research output: Contribution to journalArticleResearchpeer-review

Recursive multi-step time series forecasting by perturbing data

Taieb, S. B. & Bontempi, G., 2011, Proceedings - 11th IEEE International Conference on Data Mining, ICDM 2011. Cook, D., Pei, J., Wang, W., Zaiane, O. & Wu, X. (eds.). Piscataway NJ USA: IEEE Computer Society, p. 695-704 10 p. 6137274

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

2010

Multiple-output modeling for multi-step-ahead time series forecasting

Ben Taieb, S., Sorjamaa, A. & Bontempi, G., Jun 2010, In : Neurocomputing. 73, 10-12, p. 1950-1957 8 p.

Research output: Contribution to journalArticleResearchpeer-review