Category Archives: Blog

ISF2019 talk: Cross-temporal coherent forecasts for tourism forecasting

This year’s International Symposium on Forecasting has been a great success. Very exciting talks and large attendance from both academics and practitioners. I really enjoy conferences that the two groups interact organically: only this way research is both relevant and adopted fast, so that it makes a difference! This year I was invited by Haiyan… Read More: ISF2019 talk: Cross-temporal coherent forecasts for tourism forecasting »

Towards the “one-number forecast”

1. Introductory remarks One of the recurrent topics in online discussions on sales forecasting and demand planning is the idea of the “one-number forecast”, that is a common view of the future on which multiple plans and decisions can be made, from different functions of an organisation. In principle, this is yet another idea around… Read More: Towards the “one-number forecast” »

Tutorial for the nnfor R package

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: Tutorial for the nnfor R package »

Webinar: getting the best out of forecasting software

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.

R package: tsutils

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: R package: tsutils »

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 »

Can you predict the closing price of Bitcoin?

Lately, there has been a lot of talks whether Bitcoin is a bubble (about to burst) or not. The discussion is quite interesting, not only because there is potentially a lot of money involved, but also because it shows how our economic theories are primarily unclear and secondarily incomplete on concepts such as bubbles and… Read More: Can you predict the closing price of Bitcoin? »

MAPAx example for R

I have been asked a few time to provide an example how to use MAPAx, from the MAPA package for R, so I prepared this blog post. I admit the documentation could be better, so I put together this example from a retailing case – the original setting MAPAx was developed for (see paper here).… Read More: MAPAx example for R »

nnfor on github

I have put up a github repository for the nnfor package for R: https://github.com/trnnick/nnfor I will be putting updates and fixes there, before they are pushed on CRAN. You can also report there bugs. You can install the current github version with: Related PostsForecasting time series with neural networks in R Can neural networks predict… Read More: nnfor on github »