I have summarised several posts on point forecasts evaluation in an article for the Foresight journal. Mike Gilliland, being the Editor-in-Chief of the journal, contributed to the paper a lot, making it read much smoother, but preferred not to be included as the co-author. This article was recently published in the issue 74 for Q3:2024. … Read more
When you start working with your data and suddenly realise that there are zeroes there, i.e. it is intermittent demand, what should you do first? Some people use SBC classification, but is that what you need? Let’s discuss! Intermittent demand comes in different flavours: sometimes zeroes occur frequently with low demand volumes, while other times … Read more
Sometimes, when you need to forecast demand, you may notice that the recorded data contains zeroes. There are several possible reasons for this, but today we’ll briefly discuss one of them. Welcome to the world of “intermittent demand”! Intermittent demand is the demand that happens at irregular frequency (Svetunkov & Boylan, 2023). This means you … Read more
While there is a lot to say about multistep losses, I’ve decided to write the final post on one of them and leave the topic alone for a while. Here it goes. Last time, we discussed MSEh and TMSE, and I mentioned that both of them impose shrinkage and have some advantages and disadvantages. One … Read more
As we discussed last time, there are two possible strategies in forecasting: recursive and direct. The latter aligns with the estimation of a model using a so-called multistep loss function, such as Mean Squared Error for h-steps-ahead forecast (MSEh). But this is not the only loss function that can be efficiently used for model estimation. … Read more
Have you heard about the recursive vs direct forecasts? There’s literature about them in the areas of both ML and statistics. What’s so special about them? Here is a short post. The term “recursive” forecasting refers to the approach, when we produce one-step-ahead forecast first, then use it to produce two-steps-ahead, three-steps-ahead, and so on. … Read more
Not every pattern that appears seasonal is genuinely seasonal. This means you don’t always require a seasonal model when you see repetitive patterns with fixed periodicity. How come? First things first, in forecasting, the term “seasonality” refers to any natural pattern repeating with some periodicity. For example, if you work in a hospital with A&E … Read more
So far, we’ve discussed forecasts evaluation, focusing on the precision of point forecasts. However, there are many other dimensions in the evaluation that can provide useful information about your model’s performance. One of them is bias, which we’ll explore today. Introduction But before that, why should we bother with bias? Research suggests that bias is … Read more
What is “forecasting”? Many people will have a ready answer to this question, but I would argue that not many have spent enough time thinking about it. Should we spend a couple of minutes of our time today to do that? Straight to the point: my answer to the question comes to the following definition: … Read more
One question I received from my LinkedIn followers was how to evaluate forecast accuracy in practice. MAPE is wrong, but it is easy to use. In practice, we want something simple, informative and straightforward, but not all error measures are easy to calculate and interpret. What should we do? Here is my subjective view. Step … Read more