Tag Archives: automatic specification

Incorporating Leading Indicators into your Sales Forecasts

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: Incorporating Leading Indicators into your Sales Forecasts »

Update for nnfor: reuse and retrain models

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: Update for nnfor: reuse and retrain models »

New R package nnfor: time series forecasting with neural networks

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: New R package nnfor: time series forecasting with neural networks »

OR59 Keynote: Uncertainty in predictive modelling

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: OR59 Keynote: Uncertainty in predictive modelling »

Benchmarking Facebook’s Prophet

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: Benchmarking Facebook’s Prophet »

Tactical sales forecasting using a very large set of macroeconomic indicators

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

Demand forecasting by temporal aggregation: using optimal or multiple aggregation levels?

N. Kourentzes, B. Rostami-Tabar and D.K. Barrow, 2017, Journal of Business Research. http://doi.org/10.1016/j.jbusres.2017.04.016

Temporal Big Data for Tire Industry Tactical Sales Forecasting

Y.R. Sagaert, E-H. Aghezzaf, N. Kourentzes and B. Desmet, 2017, Interfaces. https://doi.org/10.1287/inte.2017.0901

Can neural networks predict trended time series?

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: Can neural networks predict trended time series? »

The difference between in-sample fit and forecast performance

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: The difference between in-sample fit and forecast performance »