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  • Journal Publications

  1. L.H. Kaack, J. Apt, M.G. Morgan, P.E. McSharry (2017). Empirical prediction intervals improve energy forecasting. Proceedings of the National Academy of Science 114 (33): 8752–8757. DOI: 10.1073/pnas.1619938114.
  2. Innocent Ngaruye, Joseph Nzabanita, Dietrich von Rosen & Martin Singull. (2017). Small area estimation under a multivariate linear model for repeated measures data. Communications in Statistics-Theory and Methods. http://dx.doi.org/10.1080/03610926.2016.1248784
  3. S. Brown, P.E. McSharry (2016). Improving accuracy and usability of growth charts: Case study of Rwanda. British Medical Journal Online 6: e009046.
  4. C. Njuguna, P.E. McSharry (2016). Constructing spatiotemporal poverty indices from big data. Journal of Business Research 70: 318-327.
  5. R. Löwe, H. Madsen, P.E. McSharry (2016). Objective Classification of rainfall for online operation of urban water systems based on Gaussian-inspired techniques. Water 8(3): 87.
  6. H. Oliver, P.E. McSharry (2016). A design proto pattern for continuously evaluated forecasting in IBM InfoSphere Streams. Journal of Software: Practice and Experience. DOI:10.1002/spe.2316.
  • Conference Proceedings

  1. D. Uwizera & P.E. McSharry (2017). Forecasting and monitoring maize production using satellite imagery in Rwanda. IEEE Technological Innovations in ICT for Agriculture and Rural Development, 7-8 Apr, Chennai, India. ISBN:978-1-5090-4437-5.
  2. L.H. Kaack, J. Apt, M.G. Morgan, P.E. McSharry (2016). The applicability of empirical prediction intervals to energy forecasting. Energy: Expectations and Uncertainty, 39th IAEE International Conference, Jun 19-22, 2016.
  3. D. Habamwabo & P.E. McSharry (2016). Healthcare Monitoring based on Digital

Transactions at Pharmacies: Malaria in Kigali. IEEE International Conference on

Bioinformatics and Biomedicine, 15-18 Dec, Shenzhen, China. ISBN 978-1-5090-1612-9.

  • Book chapters

  1. C. Njuguna & P.E. McSharry (2017). Predicting socioeconomic status using big data.

In Big data and human development: improved processes or a deeper digital divide?. Editors: M. Graham, S. Ojanpera, E. Lopez. Wiley, London, UK.

  1. P.E. McSharry (2017). Parsimonious risk assessment and the role of transparent diverse models. In “Risk Modeling for Hazards and Disasters”. Editor: G. Michel, Elsevier, London, UK. ISBN: 9780128040713. 
  2. J. W. Taylor and P. E. McSharry (2017). Univariate methods for short-term load forecasting. In Advances in electric power and energy: power systems engineering, Editor M. El-Hawary, IEEE Press/Wiley.

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African Center of Excellence in Data Science(ACE-DS)

Head Office:UR College of Business & Economics

E-mail:   aceds@ur.ac.rw
Phone:(+250) 788456452


Phone:(+250) 788484421