Projects per year
Personal profile
Biography
Dr. Souhaib Ben Taieb is a Lecturer (Assistant Professor) in Statistical Machine Learning at Monash University in Melbourne, Australia. His research interests include statistical machine learning, time series analysis, probabilistic forecasting, big data processing and smart grid analytics.
He received an M.Sc and a Ph.D. in Computer Science with a specialization in Machine learning from the Free University of Brussels in Belgium. He was a postdoctoral research fellow in the Spatio-Temporal and Data Science Group at KAUST. He was a visiting scholar at several international institutions, including the School of Earth, Energy and Environmental Sciences, Stanford University, and the Said Business School, University of Oxford.
Dr. Ben Taieb has received multiple awards including an Early-Career Faculty Research grant from Monash Business School, the Solvay Awards 2015 for his PhD thesis, an IEEE Power & Energy Society Award for the Global Energy Forecasting Competition 2012, and a Doctoral research fellow grant from the Belgian National Fund for Scientific Research.
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Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
Network
Projects
- 2 Finished
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Advisian Digital, Proof of Concept for Application of Advanced Short Term Power Generation Forecasting Technology for Wind and Solar Farms
Nitchov, T., Marshment, D., Bergmeir, C., Brown, S., Danka, G., Vahid-Araghi, F., Miller, B., Hyndman, R., Ben Taieb, S., Salehi, M. & Khalilpour, K.
28/05/19 → 6/01/21
Project: Research
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Multivariate Time Series Prediction Techniques with Fixed and Non-Fixed Relationship
Hyndman, R., Ben Taieb, S. & Bergmeir, C.
4/12/17 → 4/12/18
Project: Research
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Forecasting: theory and practice
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., Ellison, J., & 60 others , Jul 2022, In: International Journal of Forecasting. 38, 3, p. 705-871 167 p.Research output: Contribution to journal › Review Article › Research › peer-review
Open AccessFile116 Citations (Scopus) -
Hierarchical probabilistic forecasting of electricity demand with smart meter data
Taieb, S. B., Taylor, J. W. & Hyndman, R. J., 2021, In: Journal of the American Statistical Association. 116, p. 27-43 17 p.Research output: Contribution to journal › Article › Research › peer-review
30 Citations (Scopus) -
Non-linear mixed-effects models for time series forecasting of smart meter demand
Roach, C., Hyndman, R. & Ben Taieb, S., Sep 2021, In: Journal of Forecasting. 40, 6, p. 1118-1130 13 p.Research output: Contribution to journal › Article › Research › peer-review
2 Citations (Scopus) -
Hospital characteristics, rather than surgical volume, predict length of stay following colorectal cancer surgery
Vicendese, D., Marvelde, L. T., McNair, P. D., Whitfield, K., English, D. R., Taieb, S. B., Hyndman, R. J. & Thomas, R., Feb 2020, In: Australian and New Zealand Journal of Public Health. 44, 1, p. 73-82 10 p.Research output: Contribution to journal › Article › Research › peer-review
Open AccessFile10 Citations (Scopus) -
Predicting the whole distribution with methods for depth data analysis demonstrated on a colorectal cancer treatment study
Vicendese, D., Te Marvelde, L., McNair, P. D., Whitfield, K., English, D. R., Ben Taieb, S., Hyndman, R. J. & Thomas, R., 2019, Statistics and Data Science: Research School on Statistics and Data Science, RSSDS 2019 Proceedings. Nguyen, H. (ed.). 1st ed. Singapore Singapore: Springer, p. 162-182 21 p. (Communications in Computer and Information Science; vol. 1150).Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other › peer-review
Open AccessFile