Can you spot trend in time series?

Past experiments have demonstrated that humans (with or without formal training) are quite good at visually identifying the structure of time series. Trend is a key component, and arguably the most relevant to practice, as many of the forecasts that affect our lives have to do with potential increases or decreases of economic variables. Forecasters… Read More »

Congratulations Dr. Svetunkov!

A couple of days ago it was the graduation ceremony for MSc and PhD students at Lancaster University. Ivan Svetunkov, one of my ex-PhD students officially graduated; well done Ivan! In a previous post I described briefly part of his research. He is also working on the excellent smooth package for R. You can find… Read More »

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 »

Talk on `Advances in promotional modelling and demand forecasting’

On 16/11/2016 I gave a talk at the Stockholm School of Economics on the topic of advances in modelling and demand forecasting. Given the diversity of the audience I avoided going into the details of the mathematical formulations, some of which can be found in the appendix of the presentation. The presentation summarises three different… Read More »

MAPAx available for R & new MAPA package on CRAN

The previous version of the MAPA package implemented only the univariate aspect of the algorithm. Version 2.0.1 implements MAPAx as well, which allows incorporating regressors in your forecasts. In this paper we demonstrated the usefulness of temporal aggregation in the case of forecasting demand in the presence of promotions. In particular, we showed that MAPAx… Read More »