The nnfor (development version here) package for R facilitates time series forecasting with Multilayer Perceptrons (MLP) and Extreme Learning Machines (ELM). Currently (version 0.9.6) it does not support deep learning, though the plan is to extend this to this direction in the near future. Currently, it relies on the neuralnet package for R, which provides… Read More »
Last month Robert Fildes and I were invited to give a webinar for the Operational Research Society, on the topic of forecasting software. Slides are available here. A related discussion by Robert and colleagues can be found here.
The tsutils package for R includes functions that help with time series exploration and forecasting, that were previously included in the TStools package that is only available on github. The name change was necessary as there is another package on CRAN with the same name. The objective of TStools is to provide a development and… Read More »
Nikolaos Kourentzes and Yves Sagaert, Foresight: The International Journal of Applied Forecasting, 2018, Issue 48. This is a modified version of the paper that appears in Foresight issue 48. This provides a simplified version of the modelling methodology described in this paper and applied here and here. Introduction Using leading indicators for business forecasting has… Read More »
Yves R. Sagaert, Nikolaos Kourentzes, Stijn Du Vuyst, El-Houssaine Aghezzaf and Bram Desmet, 2018, International Journal of Production Economics.
Fotios Petropoulos, Nikolaos Kourentzes, Konstantinos Nikolopoulos and Enno Siemsen, 2018, Journal of Operations Management. https://doi.org/10.1016/j.jom.2018.05.005
Juan R. Trapero, Manuel Cardos and Nikolaos Kourentzes, 2018, International Journal of Forecasting.
Nikolaos Kourentzes, Devon K. Barrow and Fotios Petropoulos, 2018, International Journal of Production Economics. https://doi.org/10.1016/j.ijpe.2018.05.019
Juan R. Trapero, Manuel Cardos and Nikolaos Kourentzes, 2018, Omega. https://doi.org/10.1016/j.omega.2018.05.004
Nikolaos Kourentzes, Ivan Svetunkov and Stephan Kolassa, ISF2018, 20th June 2018. In doing forecast selection or combination we typically rely on some performance metric. For example, that could be Akaike Information Criterion or some cross-validated accuracy measure. From these we can either pick the top performer, or construct combination weights. There is ample empirical evidence… Read More »