Project Description

The research goal of the study is to evaluate and describe the characteristics of time series pre- diction with complex structures applied on large commercial data sets while proposing an approach for inventory planning of hardware components of a datacenter using Gaussian Process Regression, Rasmussen and Williams (2005), Optimal Transport, Villani (2008); Santambrogio (2015), and causal modeling, Pearl (2009), while critically analyzing the state of the art in the theory and the industry.

Following tasks should be carried out in a period of 2 to 4 months

  • To propose a method capable of forecasting future hardware purchases optimally based on a set of time series related to demand for resources of a data center and information on the hardware available, using Gaussian Process Regression and/or optimal transport, Bachoc et al. (2018) with causal modeling, Huang et al. (2015);
  • To review the existing theory on time series prediction and causal modeling for time series;
  • To identify the unique characteristics of the cloud computing industry for inventory planning and control;
  • 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 inventory planning 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 inventory optimization 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.