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 »
A feature that was missing in the nnfor package was the ability to reuse and retrain mlp and elm model fits. This is now possible with the new arguments model and retrain. As an example, let us use the AirPassengers time series, with three different sample sizes and re-use and re-train the same model in… Read More »
My new R package nnfor is available on CRAN. This collects the various neural network functions that appeared in TStools. See this post for demo of these functions. In summary the package includes: Automatic, semi-automatic or fully manual specification of MLP neural networks for time series modelling, that helps in specifying inputs with lags of the… Read More »
I recently presented at the OR59 conference my views and current work (with colleagues) on uncertainty in predictive modelling. I think this is a topic that deserves quite a bit of research attention, as it has substnatial implications for estimation, model selection and eventually decision making. The talk has three parts: Argue (as others before… Read More »
Last February Facebook open sourced its Prophet forecasting tool. Since, it had appeared in quite a few discussions online. A good thing about Prophet is that it one can use it very easily through R (and Python). This gave me the opportunity to benchmark it against some more standard – and not! – forecasting models… Read More »
Y.R. Sagaert, E-H. Aghezzaf, N. Kourentzes and B. Desmet, 2018. European Journal of Operational Research, 264.2 (2018): 558-569. https://doi.org/10.1016/j.ejor.2017.06.054
N. Kourentzes, B. Rostami-Tabar and D.K. Barrow, 2017, Journal of Business Research. http://doi.org/10.1016/j.jbusres.2017.04.016
Y.R. Sagaert, E-H. Aghezzaf, N. Kourentzes and B. Desmet, 2017, Interfaces. https://doi.org/10.1287/inte.2017.0901
Yes and… no! First, I should say that I am thinking of the common types of neural networks that are comprised by neurons that use some type of sigmoid transfer function, although the arguments discussed here are applicable to other types of neural networks. Before answering the question, let us first quickly summarise how typical… Read More »
One of the fundamental differences in conventional model building, for example they way textbooks introduce regression modelling, and forecasting is how the in-sample fit statistics are used. In forecasting our focus is not a good description of the past, but a (hopefully) good prediction of the yet unseen values. One does not necessarily imply the… Read More »