Over the last years I have been working (with my co-authors!) on the idea of Multiple Temporal Aggregation (MTA) for time series forecasting. A number of papers have been published introducing and developing the idea further, or testing its effectiveness for forecasting. In this series of blog posts I will try to summarise the progress… Read More »
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 »
The ABC-XYZ analysis is a very popular tool in supply chain management. It is based on the Pareto principle, i.e. the expectation that the minority of cases has a disproportional impact to the whole. This is often referred to as the 80/20 rule, with the classical example that the 80% of the wealth is owned… Read More »
Together with Fotios Petropoulos we gave a workshop on producing forecasts with R, at the International Symposium on Forecasting, 2016. You can find the material of the workshop here. The workshop notes assume knowledge of what the various forecasting methods do, which are only briefly explained in the workshop’s slides, and mostly focuses in showing… Read More »
Choosing the most appropriate forecasting method for your time series is not a trivial task and even though there has been scientific forecasting for so many decades, how to best do it is still an open research question. Nonetheless, there are some reasonable ways to deal with the problem, which although they may not be… Read More »
Extrapolative forecasting, using models such as exponential smoothing, is arguably not very complicated from a mathematical point of view, but it requires a shift in logic in terms of what is a good forecast. For this discussion I will use a simple form of exponential smoothing to demontrate my point. 1. The forecasting model: single… Read More »
From time to time people have asked me how to implement Holt Winters (trend-seasonal exponential smoothing) in Excel. I have my reservations for using Excel to do your day-to-day forecasting. Nonetheless, you can find an example here.