When I read posts written by some ML experts, I sometimes notice that they either overlook or do not clearly explain a few crucial steps in demand forecasting. In this post, I want to highlight the three most important ones based on my personal experience. First and foremost, stationarity (see the proper definition of the […]
Here is another fascinating aspect of the seasonal profile in your data: it can evolve over time due to changing consumer preferences. How so? Let me explain. I’ve worked with a couple of companies where there were some examples of data with drastically changing seasonal patterns over just a few years. For example, Before Covid […]
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 […]
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 […]
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 […]
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 […]
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 […]
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. […]
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 […]
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 […]