Project Description

Research in this area focuses on developing novel forecasting methods to support inventory and demand planning of a major provider of cloud computing. For this purpose, the focus will be on developing methods and applications in the fields of Gaussian Process (GP), Rasmussen and Williams (2005), Optimal Transport (OT), Villani (2008), Maximum Mean Discrepancy MMD), Gretton et al. (2012), and causality, Pearl (2009), or a combination thereof.

To achieve this, following tasks should be carried out in a period of 2 to 4 months

  • To develop a novel method based on Gaussian Process Regression and optimal transportbased on the work of Bachoc et al. (2018) either for long-term planning (i.e., 3+ years) of daily forecasts or for short-term planning, daily forecasts for the next 12 to 18 months with an accuracy of 80% ;
  • To review the existing theory on time series prediction;
  • To identify the unique characteristics of the cloud computing industry for demand forecasting;
  • To compare theoretically and practically the developed method against gradient boosting decision trees, Bayesian regression, multivariate adaptive regression splines, neural networks, autoregressive moving averages and support vector machines.

The object of research is uncertainty reduction in demand forecasting through causality and expert feedback for time series prediction and regression in a large data context within the industry. The subject of the research is forecasting demand using techniques for time series prediction while understanding causal relations among them.


  1. Bachoc, F., Suvorikova, A., Loubes, J.-M., and Spokoiny, V. (2018). Gaussian Process Forecast with multi- dimensional distributional entries. ArXiv e-prints.
  2. Gretton, A., Borgwardt, K. M., Rasch, M. J., Schölkopf, B., and Smola, A. (2012). A kernel two-sample test. J. Mach. Learn. Res., 13:723–773.
  3. Huang, B., Zhang, K., and Schölkopf, B. (2015). Identification of time-dependent causal model: A gaussian process treatment. In Proceedings of the 24th International Conference on Artificial Intelligence, IJCAI’15, pages 3561–3568. AAAI Press.
  4. Pearl, J. (2009). Causality: Models, Reasoning and Inference. Cambridge University Press, New York, NY, USA, 2nd edition.
  5. Rasmussen, C. E. and Williams, C. K. I. (2005). Gaussian processes for machine learning. MIT Press.
  6. Santambrogio, F. (2015). Optimal Transport for Applied Mathematicians: Calculus of Variations, PDEs, and Modeling. Progress in Nonlinear Differential Equations and Their Applications. Springer International Publishing.
  7. Villani, C. (2008). Optimal Transport: Old and New. Grundlehren der mathematischen Wissenschaften. Springer Berlin Heidelberg.