A couple of days ago, I noticed a link to the following paper in a post by Jack Rodenberg: https://arxiv.org/abs/2506.05941v1. The topic seemed interesting and relevant to my work, so I read it, only to find that the paper contains several serious flaws that compromise its findings. Let me explain. Introduction But first, why am … Read more
I’ve been acting as a reviewer lately, providing comments on papers about intermittent demand, and I’ve felt a bit frustrated by what some authors write. Let me explain. Several papers I reviewed claim that demand can be either intermittent or lumpy. They then mention the Syntetos-Boylan-Croston (SBC) classification and use the thresholds from Syntetos et … Read more
Everything is random! Your data, your model, its parameter estimates, the forecasts it produces, and even the minimum of the loss function you used. There is no such thing as a “deterministic” forecast – everything is stochastic! Whenever you work with data, you are working with a sample from a population. In some cases, this … Read more
For the last year, Anna Sroginis and I have been working on a paper, trying to modernise demand classification schemes and make them useful in the brave new era of machine learning. We have finally wrapped it up and submitted it to a peer-reviewed journal. But the temptation to share was too strong, so we … Read more
There are many different issues with capturing seasonality in time series. In this short post, I’d like to discuss one of the most annoying ones. I’m talking about the seasonal pattern that shifts over time. What I mean is that, for example, instead of having the standard number of observations in the cycle (e.g., 24 … Read more
In forecasting, the term seasonality doesn’t always mean what you think it does. It encompasses more than just patterns repeating from one season to the next. In fact, seasonality covers a wide range of periodic behaviors, and can have some issues associated with the naming conventions. Should we discuss? First things first: when we say … Read more
If someone tells you that method X solves all problems and is the best one ever, they are either lying intentionally or do not fully understand what they are talking about. There is no such thing as “the best approach for everything”. Let me explain. Consider two products sold by retailers: ice cream and bread. … Read more
Image above depicts a fashion model making a presentation about a forecasting method. I like the forecast for the final period in that image… Over the last few years, I’ve seen phrases like “LightGBM model” or “Neural Network model” on LinkedIn many times, and the statistician in me shivers every time. So, I figured it’s … Read more
I’m currently doing a literature review for one of my papers on intermittent demand forecasting with machine learning, and I’ve noticed a recurring fundamental mistake in several recently published papers, even in respectable peer-reviewed journals. The mistake? Using error measures based on the Mean Absolute Error (MAE). This is a crime against the humanity when … Read more
One thing that bothers me when I read posts on social media or papers in peer-reviewed journals is the claim that a proposed approach is “assumption-free.” In forecasting, this is never true. Such an approach is like a spherical unicorn in a vacuum (see image above). Here’s why. Every model is a simplification of reality, … Read more